2010 annual report - university of newcastle · pdf filedr fawang liu (qut) 2010 annual...

2010 ANNUAL REPORT 01

Enquiries and further information:

Laureate Professor Graham C.Goodwin Director ARC Centre of Excellence for Complex Dynamic Systems and Control

The University of Newcastle Callaghan, NSW 2308 Australia

T: +61 2 4921 7072 F: +61 2 4960 1712 E: [email protected] W: www.newcastle.edu.au/centre/cdsc

Publication details: Design: Bounce Editorial Assistant: Dianne Piefke, CDSC Printing: NCP Printing

2010 ANNUAL REPORTARC Centre of Excellence for Complex Dynamic Systems and Control

ARC Centre of Excellence for Complex Dynamic Systems and Control02

TABLE of CoNTENTS

OUR VISION 06

DIRECTOR’S REPORT 07

STAFF 08

POSTGRADUATE RESEARCH STUDENTS 09

THESES SUBMITTED IN 2010 10

UNDERGRADUATE RESEARCH STUDENTS 11

ADVISORY BOARD 12

VISITORS 14

ACADEMIC VISITORS 14 STUDENT VISITORS 15

INDUSTRIAL INTERACTIONS AND SELECTED OUTCOMES 2010 16

CONFERENCES, COURSES AND WORKSHOPS 2010 17

SEMINARS 18

SELECTED HIGHLIGHTS 2010 20

RESEARCH PROGRAMS 2010 21

A INDUSTRIAL CONTROL AND OPTIMISATION 22 A.1 OPTIMISATION BASED OPERATOR GUIDANCE (BHP BILLITON) 22 A.1.1 Sferics reduction in electromagnetic mineral exploration 22

A.2 INTEGRATED MINE PLANNING (BHP BILLITON) 24 A.2.1 Multi commodity price modelling 24 A.2.2 Estimation of commodity price modelling 24

A.3 MINING PHASE DESIGN WITH SIMULTANEOUS PRODUCTION SCHEDULING (BHP BILLITON) 25

A.4 NEXT GENERATION MODEL-BASED CONTROL TOOLS (MATRIKON) 25 A.4.1 Next generation model based control tools for cpo 25 A.4.2 Automated downtime cause classifier for Matrikon production manager 25

A.5 CSR SUGAR (INDUSTRIAL AFFILIATE) 26 A.5.1 Constrained, multivariable control of an integrated sugar mill system for economic enhancement 26 A.5.2 Cane train brake van control 26

A.6 INTELLIGENT ELECTRICITY NETWORKS (ENERGY AUSTRALIA) 27 A.6.1 Fault accommodation in electricity networks 27 A.6.2 Partial discharge localisation in power transformers 28

A.7 MARINE AND AEROSPACE SYSTEMS 29 A.7.1 Control of marine vessels with water jets (CFW Hamilton Jet & Co. New Zealand) 29 A.7.2 Gyroscopic stabilisation of marine platforms (Halcyon International, Australia) 29 A.7.3 Control of underwater vehicle dynamics 29 A.7.4 Ship motion control for offshore marine operations in training simulators (Offshore Simulator Centre AS, Norway) 30 A.7.5 Evaluation of robust autonomy for uninhabited airborne systems (UAS) (Boeing Research & Technology Australia) 30 A.7.6 Kinetic modelling and motion control of parallel robotic manipulators for improved automated manufacturing (Boeing Aerostructures, Australia) 31

A.8 VIRTUAL LABORATORIES FOR CONTROL ENGINEERING EDUCATION 32

A.9 MULTIOBJECTIVE OPTIMISATION FOR BUILDING COMFORT AND ENERGY 33


B MECHATRONICS 34 B.1 A MICROMACHINED 2DOF NANOPOSITIONER WITH INTEGRATED CAPACITIVE DISPLACEMENT SENSING 34

B.2 DESIGN CHARACTERIZATION, AND CONTROL OF A MICROMACHINED NANO POSITIONER WITH ON-CHIP ELECTROTHERMAL ACTUATION AND SENSING 36

B.3 HIGH-SPEED CYCLOID-SCAN ATOMIC FORCE MICROSCOPY 37

B.4 A COMPACT XYZ SCANNER FOR HIGH-SPEED ATOMIC FORCE MICROSCOPY 38

B.5 INTEGRATED STRAIN AND FORCE FEEDBACK FOR HIGH PERFORMANCE CONTROL OF PIEZOELECTRIC ACTUATORS 39

B.6 DUAL-STAGE VERTICAL FEEDBACK FOR HIGH-SPEED SCANNING PROBE MICROSCOPY 40

B.7 BRIDGING THE GAP BETWEEN CONVENTIONAL AND VIDEO-SPEED SCANNING PROBE MICROSCOPES 41

B.8 ROBUST CONTROL OF SPM WITH SIGNAL 42 TRANSFORMATION AND LIMITED NOISE BANDWIDTH

B.9 TRACKING CONTROL OF ARBITRARILY SHAPED 43 REFERENCES USING SIGNAL TRANSFORMATION

C CONTROL SYSTEM DESIGN 44 C.1 ROBUST MODEL PREDICTIVE CONTROL 44

C.2 CONSTRAINED MOTION PLANNING 44

C.3 FAULT TOLERANT CONTROL 45 C.3.1 Actuator fault tolerant control 45 C.3.2 Multisensor fusion fault tolerant control 45 C.3.3 MPC-based fault tolerant control of constrained multisensory linear systems 46 C.3.4 Fault tolerant control based on sensor-actuator channel switching and dwell time 46 C.3.5 Reference governor for tracking with fault detection capabilities for a class of nonlinear systems 46 C.3.6 Fault tolerant control applications 46

C.4 INVARIANT SETS AND ULTIMATE BOUNDS IN PERTURBED SYSTEMS 46

C.5 APPLICATION OF ADVANCED CONTROL TO POWER ELECTRONICS AND DRIVES 47

C.6 DESIGN OF NETWORKED CONTROL SYSTEMS USING THE ADDITIVE NOISE MODEL METHODOLOGY 47

C.7 qUANTIZED MODEL PREDICTIVE CONTROL WITH HORIZON ONE? 47

D SIGNAL PROCESSING 48 D.1 IDENTIFICATION OF LINEAR SYSTEMS SUBJECT TO COMMUNICATION CONSTRAINTS 48

D.2 HIGH-SPEED ANALOG-TO-DIGITAL CONVERTER DESIGN 48

D.3 EFFICIENT SOUND SYNTHESIS FOR REAL-TIME APPLICATIONS 49

D.4 ENCODING OF CONTINUOUS-TIME SIGNALS UNDER RECONSTRUCTION DELAY CONSTRAINTS 49

D.5 VOCAL TRACT MODEL ESTIMATION FOR SPEAKER VERIFICATION 49

D.6 POWER SYSTEM STATE ESTIMATION WITH COMMUNICATION CONSTRAINTS 50

D.7 IMPROVED CONTROL DESIGN METHODS FOR PROXIMATE TIME OPTIMAL SERVOMECHANISMS 50

D.8 CONTROL DESIGN FOR DUAL-STAGE ACTUATOR SYSTEMS 51

D.9 DEVELOPMENT OF DUAL-STAGE XY POSITIONING STAGE 51


D.10 SEqUENTIAL BAYESIAN FILTERING VIA MINIMUM DISTORTION qUANTIZATION 52

D.11 IDENTIFICATION OF LINEAR SYSTEMS HAVING NON-UNIFORM SAMPLING PERIOD 52

D.12 ACCURACY OF LINEAR MIMO MODELS OBTAINED BY MAXIMUM LIKELIHOOD ESTIMATION 53

D.13 TIME AND FREqUENCY DOMAIN MAXIMUM LIKELIHOOD ESTIMATION 53

D.14 ROBUSTNESS IN EXPERIMENTAL DESIGN 54

D.15 VARIANCE OR SPECTRAL DENSITY IN SAMPLED DATA FILTERING? 54

D.16 IDENTIFICATION OF SYSTEMS HAVING qUANTIZED OUTPUT DATA 55

D.17 VIRTUAL CLOSED LOOP IDENTIFICATION 55

E STATISTICS 56 BAYESIAN LEARNING (QUT NODE) 56 E.1 ADVANCES IN BAYESIAN METHODOLOGY 56 E.1.1 Mixture models 56

E.2 ADVANCES IN BAYESIAN COMPUTATION 57 E.2.1 Sparce matrix representation 57

E.3 ADVANCES IN BAYESIAN APPLICATIONS 58 E.3.1 Ecology 58 E.3.2 Animal science and genetics 60 E.3.3 Agriculture 61

E.4 OTHER MATHEMATICAL APPLICATIONS 61

STATISTICAL INFERENCE AND MODELLING (UON NODE) 62

E.5 CATEGORICAL DATA ANALYSIS 63 E.5.1 Graphical analysis of categorical data 63 E.5.2 Numerical analysis of categorical data 63

E.6 ANALYSING AND REPORTING CLINICAL INDICATORS USING BAYESIAN HIERARCHICAL MODELS 64

E.7 CONSENSUS PRIORS FOR BAYESIAN ANAYSIS 65

E.8 BAYESIAN HIDDEN MARKOV MODEL IN DNA SEqUENCE SEGMENTATION MODELLING 65

E.9 HIERARCHICAL APPROACHES TO META-ANALYSIS 66

E.10 SMOOTH TESTS OF GOODNESS OF FIT 66

E.11 qUANTILE DISTRIBUTIONS 67

F DISTRIBUTED SENSING AND CONTROL 68 F.1 SYNAPTIC PLSTICITY-BASED DYNAMIC MODEL FOR EPILEPTIC SEIZURES 68

F.2 POWER TRANSFORMER MODELING 69

F.3 ESTIMATION OF PARAMETERS FOR A SYNCHRONOUS MACHINE AT LIDDELL POWER STATION 70

F.4 ROBOTICS RESEARCH 70 F.4.1 Multimodal UKF localization using visual landmarks and odometery motion models 70 F.4.2 A study of manifold alignment using an artificial


acoustic dataset for robot localisation 71 F.4.3 Evaluation of walk optimisation techniques for the NAO robot 71 F.4.4 Perturbation sensing for humanoid robots using a multiclass support vector machine 71

G MATHEMATICAL SYSTEMS THEORY 72 G.1 BEYOND SPECTRUM 72

G.2 NONLINEAR ANALYSIS, OPTIMIZATION AN D FIXED-POINT THEORY 72

G.3 qUARTERNIONIC SIGNAL PROCESSING 74

G.4 SAMPLING IN PALEY-WIENER SPACES 74

PUBLICATIONS 75

BOOKS 76

BOOKS IN PREPARATION 76

BOOK CHAPTERS IN PREPARATION/TO APPEAR 76

PLENARY AND KEYNOTE ADDRESSES 76

PATENTS 77

JOURNAL PAPERS 77

JOURNAL PAPERS ACCEPTED FOR PUBLICATION 81

REFEREED CONFERENCE PAPERS 83

TECHNICAL REPORTS TO INDUSTRY 87

PERFORMANCE INDICATORS REPORT 89

INCOME AND EXPENDITURE STATEMENT 96


oUR VISIoN

To be a world leader in analysis, design and optimisation of complex dynamic systems; pursuing outstanding fundamental and applied research.


DIRECToR’S REpoRT

This has been an exciting year for our research centre with many highs but one notable low. This report summarizes the outcomes in both basic and applied research. We are particularly proud of the impact that our research work has had on industrial practice and we include a special section highlighting some of these achievements.

During 2010, I was pleased to be able to present our work at five international conferences via plenary addresses in Chile, USA, Taiwan, Sweden and China.

These Plenaries demonstrate both the breadth and depth of our research activities.

I was also honoured to receive two awards, namely the 2010 Nordic Process Control Award and the 2010 IEEE Control Systems Field Award.

This year also represents the end of our current funding cycle and thus a major effort was put into applying for new funding under the Australian Research Council Centres of Excellence scheme. Regrettably this application was unsuccessful.

The good news is that the University of Newcastle has agreed to provide us with on-going support in the form of a Priority Research Centre. This will allow us to continue some of the research programmes and to build momentum for a new research bid in a few years time.

Inevitably there will be some loss of research staff due to the changed funding regime. On the other hand, new industrial projects have been initiated with Boeing, EnergyAustralia and NSW Ambulance. The latter projects have received funding under the Australian Research Councils Linkage Project Scheme.

Since this is the final formal report for the ARC Centre of Excellence for Complex Dynamic Systems and Control, I would like to take this opportunity to thank several groups for their support and collaboration. Firstly, the Australian Research Council has provided us with base funding for many years. This has allowed the formation of our internationally renowned research team which expanded to approximately 60 people using the umbrella grant and other funds. Secondly, I want to acknowledge the fantastic team of researchers who made it all possible. Thirdly, I want to acknowledge the excellent research opportunities provided by our industrial collaborators including BHP-Billiton, Matrikon, Ericsson AB, EnergyAustralia, Aurecon, Boeing, Hatch-IAS, CSR, IBM, Austal Ships, CFW Hamilton Jet, Halcyon. Fourthly, the support of the University of Newcastle and the queensland University of Technology has been strong and ever present. I particularly want to thank the University of Newcastle who were the principal host institution, for their incredibly strong support and encouragement. Finally, we acknowledge the support and advice provided by our External Advisory Board chaired by Professor Michael Calford, DVC Research at the University of Newcastle.

We look forward to continuing our research in 2011 and beyond.

Graham C. GoodwinDirector and Laureate Professor


STAff STAff MoVEMENTS

DirectorLaureate Professor Graham Goodwin

Associate DirectorsProfessor Minyue Fu Professor S.O. Reza Moheimani

Chief Operating OfficerDr Greg Adams

Program Leaders Signal ProcessingProfessor Minyue Fu (Leader) Dr Juan-Carlos Agüero (Deputy Leader)

Control System DesignDr Maria Seron (Leader) Laureate Professor Graham Goodwin (Deputy Leader)

Bayesian Learning (QUT Node) Professor Kerrie Mengersen (Leader)Professor Ian Turner (Deputy Leader)

Industrial Control and Optimization Dr Julio Braslavsky (Leader) Associate Professor Tristan Perez (Deputy Leader)

Mechatronics Professor S.O. Reza Moheimani (Leader) Dr Andrew Fleming (Deputy Leader)

Statistical InferenceProfessor John Rayner (Leader) Dr Robert King – Associate Professor Eric Beh (Deputy Leaders)

Mathematical Systems Theory Associate Professor Brailey Sims (Leader) Dr Jose De Doná (Deputy Leader) Professor George Willis (Research Coordinator)

Distributed Sensing and Control Dr James Welsh (Leader) Professor Rick Middleton (Deputy Leader)

Industry Liaison OfficerAssociate Professor Tristan Perez

Industry Partner InvestigatorsDr Salvatore (Sam) Crisafulli (Matrikon) Dr Merab Menabde (BHP-Billiton Innovation) Dr R. Arthur M. Maddever (BHP-Billiton Innovation) Mr Peter Stone (BHP-Billiton Innovation) Mr Richard Thomas (Matrikon)

Industrial Affiliates:Aurecon Boeing Research and Technology, Australia CFW-Hamilton Jet (New Zealand) CSR Sugar Halcyon International Hatch IAS IBM Research Zurich

Other InvestigatorsLaureate Professor Jon BorweinDr Jeffrey HoganDr Peter HowleyDr Kaushik MahataDr Darfiana NurDr Daniel quevedoDr Elizabeth StojanovskiDr Frank Tuyl

CDSC Funded ResearchersDr David Allingham Dr Clair Alston (qUT)Mr Miroslav BacakDr Ali BazaeiDr Li ChaiMr O-Yeat ChanMr Pierre De LamberterieDr Alejandro DonaireDr Boris GodoyDr Katrina LauDr Fawang Liu (qUT)Dr Paula Lennon (qUT)Mr Iskandar MahmoodDr Damian MarelliDr Adrian MedioliMr Sean Moyniham (qUT)Dr Claus MüllerMs Raheleh NazariDr Jaime Peters (qUT)Dr Alejandro RojasDr Christopher Strickland (qUT)Dr Priyanka VaidyaMs Zoe van Havre (qUT)Dr Meng WangDr Xiaoli HuDr Yuenkuan YongDr Mei Mei Zhang Dr Jinchuan Zheng

Engineering StaffMr Frank Sobora

Support StaffMr John BestMrs Jayne Disney Mr Matthew FairbairnMr Jason KimberleyMr Andrew LawrenceMrs Dianne Piefke Mr Kevin MonahanMr Steven NicklinMr Gabriel NoronhaMr Yik Ren Teo

n Julio Braslavsky accepted a Senior Research Scientist position with CSIRO Energy Technology, Newcastle, in November 2010.

n Tristan Perez accepted an Associate Professor position with the School of Engineering – Discipline of Mechanics and Mechatronics. He is now the leader of the Mechatronics Program at the University of Newcastle.

n Alejandro Rojas accepted a tenured position at The University of Concepción, Chile.

n Meng Wang accepted a position with Ericsson AB, Stockholm, Sweden

n Mei Mei Zhang resigned in August to pursue other opportunities.


poSTgRADUATE RESEARCh STUDENTS

Ricardo AguileraThesis Title: “Model Predictive Control

Applied to Multicell Converters”Supervisor: Daniel quevedoCo-Supervisor: Graham Goodwin Degree: PhD

Brendan BurkeThesis Title: “Constrained, Multivariable Control of an

Integrated Sugar Mill System for Economic Enhancement”

Supervisor: Greg AdamsCo-Supervisor: Graham Goodwin Degree: ME

Bernardo CamposThesis Title: “Model Predictive Control of an Induction

Heating Furnace”Supervisor: Graham Goodwin Co-Supervisor: Maria SeronDegree: PhD

Diego Carrasco Yanez (Commenced 2010)Thesis Title: “Feedforward and Preview

in Model Predictive Control”Supervisor: Graham Goodwin Co-Supervisor: Juan Carlos AgüeroDegree: PhD

Rodrigo CarvajalThesis Title: “Iterative Subspace Expansions

for Wireless Communications”Supervisor: Juan-Carlos AgüeroCo-Supervisor: Graham GoodwinDegree: PhD

Francisco Eduardo Castillo SantosThesis Title: “Connections Between Geometrical

and Fixed Point Properties” Supervisor: Brailey SimsCo-Supervisor: George WillisDegree: PhD

Mauricio Cea GarridoThesis Title: “Scheduling and Control of WCDMA

Wireless Communications”Supervisor: Graham Goodwin Co-Supervisor: Katrina LauDegree: PhD

Ben DeanThesis Title: “Regression Techniques with the

Generalised Lambda Distribution”Supervisor: Robert KingCo-Supervisor: Peter HowleyDegree: PhD

Pierre De Lamberterie (Commenced 2010)Thesis Title: “quantification of Robust Autonomy”Supervisor: Tristan PerezCo-Supervisor: James WelshDegree: ME

Ramon Delgado (Commenced 2010)Thesis Title: “Connections Between Control Theory

and Sparsity”Supervisor: Graham Goodwin Co-Supervisor: Juan Carlos AgüeroDegree: MPhil

Margaret Donald (QUT)Thesis Title: “Bayesian Methods for Space-Time Data”Supervisor: Kerrie MengersenCo-Supervisor: Clair AlstonDegree: PhD

Matthew FairbairnThesis Title: “Feedback Control of an Atomic Force

Miscroscope”Supervisor: S.O. Reza MoheimaniCo-Supervisor: Andrew FlemingDegree: ME

Anthony Fowler (Commenced 2010)Thesis Title: “Multidimensional Vibration Energy Harvesting”Supervisor: S.O. Reza MoheimaniCo-Supervisor: Sam BehrensDegree: ME

Aaron Hammond (Commenced 2010)Thesis Title: “Design of MEMS Nanopositioner”Supervisor: S.O. Reza MoheimaniCo-Supervisor: Mehmet YuceDegree: ME

Wenbiao Hu (QUT)Thesis Title: “Bayesian Apatio-Temporal CART”Supervisor: Kerrie MengersenDegree: PhD

Lujun Ji (Commenced 2010)Thesis Title: “High Speed MEMs Based Nanopositioning”Supervisor: Mehmet YuceCo-Supervisor: S.O. Reza MoheimaniDegree: ME

Junaidi JunaidiThesis Title: “Alternative Methods of Adjusting for Study

Heterogeneity in Meta Analysis”Supervisor: Elizabeth StojanowskiCo-Supervisor: Darfiana NurDegree: PhD

He Kong (Commenced 2010)Thesis Title: “Model Predictive Control for Power Electronics”Supervisor: Graham Goodwin Co-Supervisor: Maria SeronDegree: ME

Lok Man HoThesis Title: “Fault Diagnosis and Fault-tolerant Control of

an Electric Vehicle with Independent Steering Action for Each Wheel”

Supervisor: Graham GoodwinCo-Supervisor: Jose de DonáDegree: PhD

Mikhail Konnik (Commenced 2010)Thesis Title: “Adaptive Optics”Supervisor: James WelshCo-Supervisor: P. SchreierDegree: PhD

Jason KulkThesis Title: "Anthropomorphic Stance and Walk for

Consumer Robots” Supervisor: James WelshCo-Supervisor: Ziyong ChenDegree PhD


Saeid MehrkanoonThesis Title: “Implementation of Blind Signal Separation and

Neural Network Based System for Epiloptic EEG Signal Extraction and Seizure Source Localization”

Supervisor: James WelshCo-Supervisor: Juan-Carlos AgüeroDegree: ME

Ali Mohammadi (Commenced 2010)Thesis Title: “Improvements on the MEMs Devices”Supervisor: Mehmet YuceCo-Supervisor: S.O. Reza MoheimaniDegree: ME

Andrew MorrisThesis Title: “Clifford Analysis and Image Processing”Supervisor: Jeff HoganCo-Supervisor: George WillisDegree: PhD

Raheleh NazariThesis Title: “Fault Tolerant Control of Uncertain Systems”Supervisor: Maria SeronCo-Supervisor: Jose De DonáDegree: PhD

Cristian PerfumoThesis Title: “Multiobjective Optimisation for Building Comfort

and Energy”Supervisor: Julio BraslavskyCo-Supervisor: John Ward (CSIRO Energy Technology)Degree: PhD

Paul RipponThesis Title: “Application of Smooth Tests of Goodness of Fit

to Generalised Linear Models”Supervisor: John RaynerCo-Supervisor: Frank TuylDegree: PhD

Michael Rose (Commenced 2010)Thesis Title: “New Methods in Experimental Mathematics”Supervisor: Johathan BorweinCo-Supervisor: Brailey SimsDegree: MPhil

Eduardo RohrThesis Title: “State Estimation in Networked Control

Systems”Supervisor: Minyue FuCo-Supervisor: Damián MarelliDegree: PhD

Aurelio T. SaltonThesis title: “Preview Control for Dual-Stage Actuators”Supervisor: Minyue FuCo-Supervisor: Zhiyong ChenDegree: PhD

Ian SearstonThesis Title: “Analysis in Geodesic Metric Spaces”Supervisor: Brailey SimsCo-Supervisor: George WillisDegree: PhD

Matthew SkerrittThesis Title: “Projection of Algorithms in the Absence of

Convexity”Supervisor: Brailey Sims Co Supervisor: Jonathan BorweinDegree: MPhil

Fajar SuryawanThesis Title: “Nonlinear Model Predictive Control”Supervisor: Jose De DonáCo-Supervisor: Maria SeronDegree: PhD

Xin TaiThesis Title: “State Estimation for Power Networks with

Communication Constraints”Supervisor: Minyue FuCo-supervisor: Damian MarelliDegree: PhD

Sri Astuti Thamrin (QUT)Thesis Title: “Bayesian Survival Analysis using Microarray

Data”Supervisor: Kerrie MengersenCo-Supervisor: J. McGreeDegree: PhD

Sachin Wadikhaye (Commenced 2010)Thesis Title: Design, Modelling and Control of Flexure-guided

Nanopositioners”Supervisor: S.O. Reza MoheimaniCo-Supervisor: Yuen Kuan YongDegree: PhD

James WanThesis Title: TBASupervisor: Jonathan BorweinCo-Supervisor: Wadin ZudilinDegree: PhD

Alain Yetendje-LemegniThesis Title: “Fault Tolerant Multisensory SystemsSupervisor: Maria SeronCo-Supervisor Jose De DonáDegree: PhD

TheSeS SUBMITTeD IN 2010

Stephen AllenThesis Title: “Higher Rank Graphs and Universal Properties”Supervisor: George WillisCo-Supervisor: David PaskDegree: PhD

David Gibson Thesis Title: “Threshold AR Models in Finance: A

Comparative Approach”Supervisor: Darfiana NurDegree: MStat

Iskandar MahmoodThesis Title: “System Identification and Robust Control of

Spatially Distributed Systems” Supervisor: S.O. Reza MoheimaniCo-Supervisor: Brett NinnessDegree: PhD

poSTgRADUATE RESEARCh STUDENTS CONTINUED


UNDERgRADUATE RESEARCh STUDENTS

Hal Cooper CDSC Industrial Development Scholarship StudentProject Title: “Power Transformer Modelling”Supervisor: James Welsh

David Ferris University of Newcastle Industry Scholarship Student (UNISS)Project Title: “Virtual Laboratory for Bioreactors”Supervisor: Graham Goodwin

Stephen Moore CDSC Summer Scholarship Research StudentProject Title: “Reconfigurable Control”Supervisor: James Welsh

hONOUrS STUDeNTS

Stephen AbelProject Title: “Mathematical Demosaicing Techniques for Natural Images”Supervisor: Jeff Hogan

Chris BanksProject Title: “Locally Primitive Groups Acting on Trees”Supervisor: George Willis

Ben BrawnProject Title: “Elementary Description of the Structure of p-adic Lie Groups”Supervisor: George Willis

Ng Wye CheongProject Title: “Facial Expression Recognition”Supervisor: James Welsh

Nicholas Hinton Project Title: “Power System Modelling and simulation for Myuna Bay Colliery”Supervisor: James Welsh

Lau Yong FongProject Title: “Computer Vision Gesture Recognition System”Supervisor: James Welsh

Manu MohanProject Title: “Modern techniques for Model Predictive Control”Supervisor: Jose De Doná

Kevin MonaghanProject Title: “An assessment of hierarchical models for monitoring healthcare”Supervisor: Peter Howley

Soh Lee ChunProject Title: “Chipless RFID Technology Study and System Design”Supervisor: James Welsh

Alexander SmithProject Title: “Adaptive Optics”Supervisor: James Welsh

Yik Ren TeoProject Title: “Vertical Feedback Controller for an Atomic Force Microscope”Supervisor: Andrew Fleming

James Wilson Project Title: “Modelling and Simulation of the John Hunter Emergency Department”Supervisor: James Welsh

Zaw Thet AungProject Title: “Computer Vision Gesture Recognition System”Supervisor: James Welsh


ADVISoRy BoARD


CHAIRMAN

Professor M. CalfordDeputy Vice-Chancellor, Research, The University of Newcastle

CURRENT MEMBERS

Distinguished Professor B.D.O. AndersonResearch School of Information Sciences and Engineering, The Australian National University, Canberra, ACT

Professor A. CareyMathematical Sciences, Australian National University, Canberra, ACT

Professor J. CarterPro. Vice-Chancellor, Faculty of Engineering and Built Environment, The University of Newcastle, Callaghan, NSW

Dr S. CrisafulliManaging Director, Matrikon, Asia Pacific Newcastle

Dr W.J. EdwardsIndustrial Automation Services Pty. Ltd., Teralba, NSW

Dr S. GaleaCSIRO, DSTO, Melbourne, Victoria

Mr R. HayesShell Refining (Australia) Pty. Ltd., Clyde Refinery, Rosehill, NSW

Professor W. HogarthPro. Vice-Chancellor, Faculty of Science and Information Technology, The University of Newcastle, Callaghan, NSW

Professor R. JarvisDepartment of Electrical and Computer Systems Engineering, Monash University, Melbourne, Victoria

Dr B. JenkinsChief Executive Officer, Newcastle Innovation Callaghan, NSW

Mr R. PeirceTechnical Systems, CSR Victoria Mill, Ingham, queensland

Professor I.R. PetersenAustralian Defence Force Academy, UNSW, Canberra, ACT

Professor A. SharmaDeputy Vice-Chancellor, queensland University of Technology

Mr C. ThewNSW Department of Industry and Investment, Sydney, NSW

VISIToRS

ACADEMIC VISITORS

Professor Frank AllgöwerUniversity of Stuttgart, Germany July-December 2010

Assistant Professor Trond AndressenNorwegian University of Science and Technology, Trondheim, Norway August 2010-August 2011

Professor Michael BarnsleyAustralian National University, Canberra. June 2010

Dr Bharath BhikkajiIndian Institute of Technology Madras, Chennai, India May-August 2010

Dr Linda BrusEricsson AB, Stockholm, Sweden November-December 2010

Professor Marco Campi University of Brescia, Italy September 2010

Professor Joe DiestelKent State University, USA September-October 2010

Professor Arie FeuerThe Technioin, Haifa, Israel July-October 2010

Professor Frank GarvanUniversity of Florida, USA. July 2010

Professor Kazimierz GoebelMaria Curie-Sklodowska University, Lublin, Poland September-October 2010

Professor Martin GroetschelTechnische University Berlin, Institute of Mathematics, Germany October 2010

Professor Thomas GustafssonLulea University of Technology, Sweden October 2009-March 2010

Dr Hernan HaimovichNational University of Rosario, Argentina November 2010-February 2011

Professor Florence JulesUniversité des Antilles et de la Guyane, France and Bulgarian Academy of Sciences August 2010

Dr Julian CaleyAustralian Institute of Marine Science, Townsville, Australia

Professor Marc LossondeUniversité des Antilles et de la Guyane, France and Bulgarian Academy of Sciences August 2010

Dr Ernesto KofmanNational University of Rosario, Argentina December 2010-February 2011

Associate Professor Wayne LawtonNational University of Singapore September 2010

Professor Anthony LauUniversity of Alberta, Canada September-October 2010

Professor Bengt LennartsonChalmers University of Technology, Göteborg, Sweden November 2010-February 2011

Dr Maicon Marques AlvesUniversidade Federal de Santa Catarina, July 2010

Professor David MayneImperial College, London, UK March 2010

Professor John Mumford Imperial College London, UK. (Visited CDSC-qUT)

Dr Megan QuinlanImperial College London, UK (visited CDSC-qUT)

Professor Julian RevalskiUniversité des Antilles et de la Guyane, France and Bulgarian Academy of Sciences August 2010

Dr Terry RockafellarUniversity of Washington, USA February 2010

Dr Sinai RobinsNanyang Technological University, Singapore May-June 2010

Dr Monica RomeroNational University of Rosario, Argentina October-November 2010

Dr Wei TangNorthwestern Polytechnical University, Peoples Republic of China August 2009-August 2010

Dr Francis ValentinisMaritime Platforms Division Defence Science and Technology Organisation Fishermans Bend, VIC. November 2010

Professor Bjorn WittenmarkUniversity of Lund, Sweden October-November 2010

Professor Steve WrightUniversity of Wisconsin, USA June-July 2010

Dr Juan YuzUniversidad Técnica Federico Santa María, Valparaiso, Chile June-July 2010

STUDENT VISITORS

Mr Gijs Hilhorst Department of Applied Mathematics, University of Twente, The NetherlandsJuly-December 2010

Mr Jaap Nieuwenhuijsen Eindhoven University of Technology, The NetherlandsNovember 2009 – January 2010

Mr Keyou YouNanyang Technological University, Singapore May – June 2010

Mr Armin StraubTulane University, New Orleans, USA August-November, 2010


INDUSTRIAL INTERACTIoN AND SELECTED oUTCoMES 2010

A prime goal of CDSC is to combine outstanding fundamental and applied research to back Australia’s industrial competitiveness and capabilities. This section reports selected outcomes achieved in collaboration with our industrial partners during 2010 and on our outreach efforts to schools and undergraduate students.

Support from the NSW Department of Industry and InvestmentWe greatly appreciate and acknowledge the support of the NSW Department of Industry and Investment. In 2010, funds were used in the following ways:

n Funding of the CDSC Industrial Development Scholarship to Hal Cooper

n Boris Godoy was partially supported to work on system identification research.

Industrial InteractionDuring 2010:

n Graham Goodwin and Adrian Medioli were awarded a Linkage Project investigating scheduling of ambulance services (partnering the NSW Ambulance Research Institute).

n Minyue Fu is part of a Linkage Project investigating communication aspects in smart electricity grids (partnering EnergyAustralia).

COMMENTS BY INDUSTRIAL PARTNER

Matrikon (Asia-Pacific) Pty. Ltd.A Matrikon client in Germany recently installed Control Performance Optimizer (CPO) on a paper mill. CPOmpc, the model predictive controller developed with CDSC expertise, was implemented to control the bark boiler, which uses waste bark material to generate power for the remainder of the mill.

This installation of CPOmpc was very successful. Although the details are confidential, some particular improvements include:n Elimination of all oscillations

in process variablesn Increase of 3.5% in steam productionn Increase of 3.5% in electrical

power generationn Reduction of boiler emissions by 20%n Reduction of boiler temperature

variations by 20%n Savings of >EUR 800,000 p.a.

(>$1Million)n Return on investment < 3 monthsn Project time: 6 months in total.

This project demonstrated the high value of collaboration between Matrikon and CDSC over the past eight years, in developing an advanced control product (CPOmpc) able to deliver real value to our clients’ operations.

Richard ThomasManager, Research and Development Matrikon (Asia-Pacific) Pty Ltd

OUTREACH TO SCHOOLS AND UNDERGRADUATE STUDENTSSince its inception, CDSC has made a special effort to interact with students in local and international schools and students engaged in undergraduate courses. This has been aimed at enthusing students with the idea of pursuing careers in science or engineering.

In 2010, Hal Cooper continued working on CDSC projects, namely Power Transformer Modeling (See Section F.2).

Beginning in 2009, CDSC participated in the University of Newcastle Industrial Scholarship Scheme (UNISS). Under this scheme, undergraduate students receive a yearly stipend. In return they are required to spend 10 weeks per year working with one of the sponsoring companies.

Our UNISS scholar is Mr. David Ferris, who has just completed second year of a joint Electrical Engineering/ Mathematics degree at the University of Newcastle (and being only 16 years old). He worked on the Virtual Laboratories for Control System Design project during the 2010/2011 university vacation period.

In 2010 a new module in the Virtual Laboratory has been designed around the concept of Bioreactor control (see below). Students learn how the Temperature, pH, dissolved oxygen and supplied substrate are controlled to maximize bacterial growth. This contrasts with most of the previous laboratories, as it is the first to address the Chemical Engineering challenges that control engineers face.

Also, in 2010, a contract was signed with quanser Inc. to act as exclusive world wide agents for the Virtual Laboratory System.


CoNfERENCES, CoURSES AND WoRKShopS 2010

n Tristan Perez delivered a two day course on “Marine Control Systems”, at the Department of Automation at Universita Politecnica delle Marche, Ancona, Italy, 9-10 September 2010.

n Graham Goodwin presented a 15 hour course on Sampling and quantization in Signals and Systems at NTNU, Norway, between 4-8 October 2010.

n Kerrie Mengersen presented “Introduction to Bayesian Analysis” short courses as follows:

– 3 day course in Kuala Lumpur, Malaysia; and

– 3 day course in Fremantle, WA

n Kerrie Mengersen presented a 4 day “Elicitator” course in Darwin, NT.

n Kerrie Mengersen organized the “Bayes on the Beach Annual Workshop” which was held in Coolangatta, queensland, 4-5 October 2010.

n Kerry Mengersen and Clair Alston gave a 4 day Workshop in Perth in November 2010 entitled “Bayesian Statistics for Animal Scientists”


SEMINARS 2010

19 February Author: Dr Sandra Hirche Technische Universität München, Munich, Germany.Title: “CONTROL AND HUMAN FACTOR ASPECTS OF TELEROBOTICS OVER THE INTERNET”

26 MarchAuthor: Dr Frank Tuyl The University of Newcastle, AustraliaTitle: “CONDITIONAL COVERAGE: ANOTHER ARGUMENT IN FAVOUR OF THE BAYES- LAPLACE PRIOR”

30 AprilAuthor: Dr Alan GoreTitle: “REFLECTIONS ON A DECADE IN INDUSTRY”

25 MayAuthor: Dr Robert Schmid The University of Melbourne, AustraliaTitle: “A UNIFIED METHOD FOR THE DESIGN OF NON- OVERSHOOTING LINEAR MULTIVARIABLE TRACKING CONTROLLERS”

28 MayAuthor: Associate Professor Eric Beh The University of Newcastle, AustraliaTitle: “A VERY CONCISE REVIEW OF ASSOCIATION MEASURES FOR CONTINGENCY TABLES”

4 June Author: Mr Mauricio Cea The University of Newcastle, AustraliaTitle: “NONLINEAR FILTERING APPLIED TO 3G SYSTEMS”

16 JulyAuthor: Mr Junaidi (PhD student) The University of Newcastle, AustraliaTitle: “ADJUSTING FOR HETEROGENEITY IN META-ANALYSIS”

6 AugustAuthor: Professor John Rayner The University of Newcastle, AustraliaTitle: “TESTING EqUALITY OF VARIANCES FOR TWO UNIVARIATE POPULATIONS”

1 SeptemberAuthor: Professor Arie Feuer Electrical Engineering Department, Technion, IsraelTitle: “COMPRESSED SENSING APPLICATION TO SAMPLING AND RECONSTRUCTION OF MULTIBAND SIGNALS”

3 SeptemberAuthor: Dr Robert King The University of Newcastle, AustraliaTitle: “A JOINT STATISTICAL MEETING SPECIAL, INCLUDING “BEYOND TECHNICAL CONFIDENTIALITY: ENABLING INTERAGENCY DATA SHARING AS A MEANS OF ENHANCING REGIONAL GOVERNANCE””

9 SeptemberAuthor: Professor Marco Campi University of Brescia, ItalyTitle: “VARIABLE ROBUSTNESS CONTROL: PRINCIPLES AND ALGORITHMS”

10 SeptemberAuthor: Assistant Professor Trond Andresen The Norwegian University of Science and Technology Trondheim, NorwayTitle: “MODELING THE GLOBAL FINANCIAL CRISIS: WHEN MANDATORY EXPENDITURE OVERWHELMS DISCRETIONARY EXPENDITURE”

Research students and staff from the Centre and The University of Newcastle as well as Australian and international visitors participate in the Centre’s seminar series. Seminars presented in 2010 follow:


15 SeptemberAuthor: Professor Sirish L. Shah University of Alberta, Edmonton, CanadaTitle: “THE NEW ROLE OF DIGITAL AUTOMATION SYSTEMS IN PROCESS MONITORING: SENSOR- FUSION AND SIGNAL PROCESSING FOR PLANT HEALTH MANAGEMENT”

16 September Author: Dr Manuel Olivares Universidad Tecnica Federico Santa Maria, Valparaiso, ChileTitle: “AUTOMATION AND INDUSTRIAL ROBOTICS”

22 SeptemberAuthor: Mr Eduardo Rohr The University of Newcastle, Australia Title: “STATE ESTIMATION IN NETWORKED CONTROL SYSTEMS”

1 October Author: Dr Trevor Moffiet and Dr Barrie Stokes The University of Newcastle, Australia Title: “RECENT DEVELOPMENTS IN BIVARIATE RELATIONSHIP MODELLING”

19 OctoberAuthor: Assistant Professor Trond Andresen The Norwegian University of Science and Technology Trondheim, Norway Title: “MACRO FINANCE FOR ENGINEERS: TODAY’S FINANCIAL AND MONETARY MACROECONOMIC SYSTEM PORTRAYED THROUGH A SIMPLE THREE-STATE BLOCK DIAGRAM”

20 October World Statistics DayAuthor: Dr Peter Hall and Professor Peter Hall University of Melbourne, Australia Title: “CONTEMPORARY STATISTICAL PROBLEMS”

Author: Dr Melanie Bahlo Walter & Eliza Hall Institute of Medical Research, Melbourne, Australia Title: “STATISTICAL APPLICATIONS OF GENETICS”

Author: Dr Christine O’Keefe CSIRO Mathematics, Informatics and Statistics, Sydney, Australia Title: “PRIVACY AND CONFIDENTIALITY IN NATIONAL STATISTICAL AGENCIES”

25 OctoberAuthor: Mr Xin Tai The University of Newcastle, Australia Title: “POWER SYSTEM STATE ESTIMATION WITH COMMUNICATION CONSTRAINT”

4 NovemberAuthor: Mr Iman Shames Australian National University, Canberra, Australia Title: “NODES MISBEHAVING IN NETWORKS: A VICE OR A VIRTUE?”

5 NovemberAuthor: Professor Björn Wittenmark Lund University, Sweden Title: “On a Windy Road with Adaptive Control”

Author: Dr Anne B. Koehler Miami University, USA Title: “FORECASTING COMPOSITIONAL TIME SERIES WITH EXPONENTIAL SMOOTHING METHODS”

8 NovemberAuthor: Mr Saeid Mehrkanoon The University of Newcastle, Australia Title: “EARLY DETECTION OF EPILEPTIC SEIZURE ONSET BY ANALYSING CHANGES IN SCALP EEG DYNAMICS”

15 NovemberAuthor: Professor Frank Allgöwer Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany Title: “LIVE & LET DIE – A SYSTEMS BIOLOGY VIEW ON CELL DEATH”

17 NovemberAuthor: Professor Frank Allgöwer Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany Title: “DIMENSIONS OF COMPLEXITY IN INTERCONNECTED SYSTEMS: LIMITATIONS AND NEW PERSPECTIVES”

8 DecemberAuthor: Dr Hernan Haimovich National University of Rosario (UNR), Argentina Title: “STABILISATION OF SWITCHING SYSTEMS VIA ITERATIVE APPROXIMATE EIGENVECTOR ASSIGNMENT”


SELECTED hIghLIghTS 2010

n Graham Goodwin was awarded the 2010 IEEE Control Systems Field Award for contributions to the theory and practice of digital and adaptive control. This is the Society’s highest award.

n Graham Goodwin delivered five Plenary Addresses at ICIT2010, (Valparaiso, Chile); ACC2010, (Baltimore); SICE2010, (Taipei, Taiwan); NPCW2010, (Lund, Sweden); and ICOTA8, (Shanghai, China).

n Graham Goodwin was awarded the 2010 Nordic Process Control Award.

n Associate Professor Tristan Perez has been recognised with an Inaugural Hunter Aerospace Industry Award in the Research and Development category. The recent Hunter Aerospace Industry summit united industry and education sectors related to aerospace within the Hunter region. Associate Professor Perez was nominated for leading the research work on Robust Autonomy of Uninhabited Airborne Systems (UAS). This work is being conducted in collaboration with Boeing Research and Technology Australia.

n Tristan Perez has become a member of Engineers Australia National Panel on Mechatronics.

Tristan Perez, Plenary Address at 8th IFAC Conference on Control Applications in Marine Systems (CSMS), Rostock-Wandermunde, Germany.

Graham Goodwin delivering a Plenary Address at the International Conference on Optimization: Techniques and Applications, held in Shanghai, P.R. China.


RESEARCh pRogRAMS


Program Goals:

The partnerships between researchers and industry enable reciprocal transfer of knowledge and new ideas of great potential impact on the community and economy. This Program encompasses several research projects motivated by and in collaboration with industrial partners. The main underlying theme of these projects is the application of advanced control and optimisation techniques to maximise asset utilisation and production in selected industrial processes of significant complexity. The complexity of the dynamics of such processes arise from factors including model errors, unknown disturbances, nonlinearities, distributed parameter systems, elements of Human-Machine Interaction and hybrid (Discrete and Continuous State) components. Expected outcomes of the Program include high quality research solutions and human resources tailored to the needs of Australian industry.

A.1 OPTIMISATION BASED OPERATOR GUIDANCE (BHP BILLITON)

Leader: Julio Braslavsky

Researchers: Katrina Lau Graham Goodwin

Industrial Collaborators: Arthur Maddever (Resource Business Optimisation, BHP-Billiton)

A.1.1 Sferics Reduction in Electromagnetic Mineral Exploration

Researchers: Julio Braslavsky Graham Goodwin Katrina Lau

External Collaborator: Arthur Maddever (Resource R&D, BHP-Billiton)

The aim of this project is to enhance the signal-to-noise ratio of measurements made in mineral exploration techniques. The original focus of the project was the reduction of sferics noise (electromagnetic noise originating from lightning storms). In recent years, the scope has been broadened to include a wider range of sensors and other forms of interference.

A. INDUSTRIAL CoNTRoL AND opTIMISATIoN

Julio BraslavskyProgram Leader

Tristan PerezDeputy Program Leader


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In September, a paper on errors-in-variables techniques with application to mineral exploration was accepted by Automatica. The paper describes a model estimation algorithm which exploits non-stationarity. The algorithm is successfully applied to model estimation for sferics noise cancellation.

This year, the focus of the project has been broadened to include other types of sensors such as SqUIDs and total field sensors. Another problem which was studied is the removal of powerline interference from the measured responses. The fundamental (50Hz) component or this interference is particularly difficult to remove because it is characterised by a broad peak at 50 Hz. The width of the peak implies that the effectiveness of a simple notch filter is limited as the noise cannot be removed without also removing a significant part of the signal. The broad peak is caused by variations in the frequency, amplitude and phase of the waveform, and so, adaptive noise cancellation techniques have been developed and tested on this problem – see Figure 1.Figure 1: Spectrum of the signal before and after performing interference

cancellation to remove the broad peak at 50 Hz.


A.2 INTEGRATED MINE PLANNING (BHP BILLITON)

Researchers: Tristan Perez Meimei Zhang Boris Godoy Graham C. Goodwin

A.2.1 Multi Commodity Price Modelling

Researcher: Tristan Perez

This project investigated a model structure that could extend the Schwartz and Smith model for single commodity price to a model for 2 commodities. The most difficult aspect of adequate model structure selection are a variance-bias trade off (over/under parameterisation), identifiability, and co-integration. The latter describes multivariate time series models where the components are unit-root non-stationary, but there exist linear combinations of the series components that are stationary. Co-integration suggests that the series have a common trend terms, and thus there is a long-term equilibrium between some of the components. Simple tests made on market data in Copper and aluminum spot prices showed that these time series are co-integrated.

Further analysis showed that the 1-lag difference of the series was indeed uncorrelated. This suggested a structure similar to that of Schwartz and Smith, but with specific coupling terms. The extension to the model to future prices still remains to be researched.

A.2.2 Estimation of Commodity Price Modelling

Researcher: Boris Godoy

Parameter estimation in the commodity price model using real copper data One major focus in commodity price modelling is the development of algorithms for the estimation of parameters in the Schwartz-Smith model. We have achieved this goal in previous years using new algorithms on simulated data. During 2010, we have applied our algorithms to real data for copper. The data describes the evolution of the price of copper in the last 17 years, based on weekly sampling, and considers the spot price and also future prices at 3, 15 and 27 months.

The proposed algorithm, when applied to the real data for copper prices, shows that all the parameters in the Schwartz-Smith model can be successfully estimated. Also, the data clearly shows that the system experienced a radical change in 2004. Hence, data prior to this time is not helpful as a way of generating price scenarios at the present time. To compare the influence of these changes of the data, we have carried out the estimation of the parameters considering 3 subsets for the data: Jul ‘93-Dec ‘03, Jan ‘04-Dec 09, and Jul ‘93-Dec 09. The results obtained are shown in Figure 2, and indicate the need to reduce the weighting on old values of the data in order to preserve accuracy in the estimates.

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Figure 3: For the prices of copper since July ’93 to Dec. ’09 in the London Metal Exchange (LME). These data considers spot and future prices at 3, 15 and 27 months.

Figure 2: Value for the estimated parameters of the Schwartz-Smith model considering three cases: (i) Jul ‘03- Dec ‘03; (ii) Jan ‘04-Dec ‘09; (iii) Jul ‘03- Dec ‘09.


A.2.3 Mining Phase Design with Simultaneous Production Scheduling (BHP Billiton)

Researcher: Meimei Zhang

Open pit mine planning is a process used to determine the extraction time and the economic cutoff grade of a material resource subject to a set of geotechnical and capacity constraints, and aiming at maximising the net present value (NPV) over the life of a mine.

Traditionally, the overall mine planning process starts with the determination of the ultimate pit limit. Material within the ultimate pit limit is then divided into mining phases followed by calculation of a production schedule. In open pit mine planning, mining phase design is a strategic driver of the production schedule and determines the annual net cash flow distribution of a mining project.

The most frequently used commercial mining phase design approach, such as Whittle’s nested pit method, is usually determined by aggregating Lerchs-Grossman shells. In BHP Billiton’s proprietary mine planning tool Blasor, blocks aggregates are first scheduled before creating the mining phases. The estimated block extraction time determined by the prior block aggregate schedule is then used to guide the construction of mining phases by clustering proximate blocks in terms of the estimated extraction time as well as the coordinates of their centroids.

While these approaches can create mining phases efficiently, decoupling mining phase design and panel scheduling may cause a sub-optimal NPV as ideally the goodness of a mining phase design should be evaluated by the resulting NPV generated by panel scheduling, rather than anything else.

In this project we have carried out a preliminary simulation study and computational analysis on how mining phases can be determined simultaneously with panel scheduling. The test results show that the MILP formulation cannot be solved within a reasonable computational time frame. This is mainly due to a large integer solution tree caused by two sets of difficult constraints, which force (1) the blocks within each individual mining phase to be contiguous, and (2) the cutoff grade and extraction period decisions to be made on a panel basis rather than on a block basis.

A.3 NEXT GENERATION MODEL-BASED CONTROL TOOLS (MATRIKON)

A.3.1 Next Generation Model Based Control Tools for CPO

Researchers: Greg Adams Graham Goodwin Adrian Medioli Maria Seron James Welsh

External Collaborator: Richard Thomas (Matrikon)

The aim of this project is to deliver to Matrikon process control tools that allow

n appropriate handling of complex, nonlinear and heterogeneous processes;

n robust and easy-to-use system identification; and

n economic optimisation of process variables.

Progress in 2010 occurred in the following areas:

n Matrikon completed the development of CPOmpc control solution platform with an overseas company, which involves the control of non-linear systems via multiple linear regions. See Industrial Outcomes section.

n The derivation of MPC tuning parameters, from parameters of existing (non-MPC) control, was continued.

n A robust MPC algorithm from Lovaas, Seron and Goodwin is under investigation for possible implementation.

A.3.2 Automated Downtime Cause Classifier for Matrikon Production Manager

Researchers: Adrian Medioli

External Collaborators: Pat Farragher (Matrikon) Levi Jardim (Matrikon) Damien Francois (Université Catholique de Louvain, Belgium)

This project involves the development of algorithms for automatically generating downtime causes from alarm sets. CDSC work in 2010 involved:

n Strategies for culling irrelevant data.

n Different handling of data pre – and post-downtime.

n Implementing better algorithms in the adaptive centroid scheme to improve performance.

n Delivery of the completed package to Matrikon, for inclusion in Matrikon’s Production Manager.


A.4 CSR SUGAR (INDUSTRIAL AFFILIATE)

A.4.1 Constrained, Multivariable Control of an Integrat-ed Sugar Mill System for Economic Enhancement

Researchers: Greg Adams Brendan Burke (CSR Sugar, Student) Graham Goodwin Alejandro Rojas

External Collaborators: Rob Peirce (CSR Sugar)

CSR Sugar’s Pioneer Mill (near Ayr, North queensland) co-generation plant uses waste cane fibre (bagasse) from a number of mills to create steam for both sugar milling and for electricity generation. If steam is used efficiently in sugar milling, CSR can export 50MW of power to the local grid, gaining an income stream from a waste product and reducing carbon emissions.

This project aims to study energy and steam use in sugar processing at Pioneer Mill.

The focus in 2010 was on the mill optimisation task. Brendan Burke, as part of his postgraduate project, looked at quantifying the effects of different process operations (e.g. syrup heating) on overall steam usage, in order to derive objective function sensitivities. Modelling work on the process units has been done, and a multiple objective function framework, considering steam conservation, bagasse usage, and export power, is under investigation.

Future work will focus on collecting the above work together into a mill optimisation problem, and solving this for Pioneer Mill. This will ultimately form a practical mill optimiser for the mill operators to consult on a day-to-day basis.

A.4.2 Cane Train Brake Van Control

Researchers: Brailey Sims Callum Stuart (Student)

Sugar cane trains use a wagon at the end of the train, called a brake van, to control train braking. The main aim of the brake van is to keep the couplings between each of the cane bins in tension. Once the couplings go into compression, derailments can occur, especially when the bins are empty. The operation of the brake van is via radio link from the locomotive, and consists of a numbered dial with increasing amounts of brake pressure. A park brake can also be independently applied.

CSR is looking to improve the control of the brake van with an automated process (i.e. the brake van automatically selecting the appropriate level of braking for the conditions), as well as implementing a new braking unit. In making these improvements, CSR is looking to increase the efficiency of the whole process of transporting cane. By automating the braking system they

will effectively save money on driver training, reduce fuel needs (since the brakes are being used more effectively) and cut down on the number of replacements to the brake pads. Replacing the current brake type with an electrical braking system will save further, since electrical systems brake much faster than the current system.

Work performed by CDSC on this project in 2010, by Callum Stuart, involved modelling of the forces involved in the carriages/couplings, and optimisation of the train operation for numerous track profiles (straight, curved, uphill and downhill, and combinations of these). Such profiles can be calculated for any track profile, based on the train braking model. Further work may involve development of a system for automated braking, and pre-planning of journeys with dynamic control to maintain optimal braking force at each part of the journey.

Figure 4 shows the optimised braking force at four-metre intervals for one particular track profile.

Figure 4: Modelling of forces as a function of distance in one particular track profile.


A.5 INTELLIGENT ELECTRICITY NETWORKS (ENERGYAUSTRALIA)

These projects are funded in CDSC using funds from the University of Newcastle’s Centre for Intelligent Electricity Networks, a research centre set up in the School of Electrical Engineering and Computer Science, and funded by EnergyAustralia.

A.5.1 Fault Accommodation in Electricity Networks

Researchers: Julio Braslavsky Greg Adams Maria Seron

An important question in future electricity networks is the enhancement of existing (or development of new) voltage regulation schemes in the subtransmission and distribution networks, to incorporate the following desirable characteristics:

Subtransmission network

n coordinated regulation, to avoid detrimental interactions such as regulating schemes chasing one another (runaway effects and oscillations)

n flexibility and robustness, to cope with dynamic load variations and network reconfiguration

Distribution network

n integration of distributed generation, taking advantage of real-time communication with distributed generation sources;

n integration of distributed sensor/actuator networks, taking advantage of increased real-time distributed voltage information and control authority.

Work in 2010 focused on scoping research directions to address voltage regulation issues in future energy networks. The main target is to explore voltage regulation strategies at subtransmission and distribution network levels. One main challenge in such networks is to incorporate distributed sensor information to improve quality and reliability of the power supply in the face of a higher penetration of distributed generation and intermittent renewable sources of energy. Five technical reports have been produced to date as well as a proposal for an ARC linkage project grant, to be submitted in 2011.

At this stage, a preliminary simulation model has been developed and tested using measured data provided by EnergyAustralia. Utilising this model, it has been shown how real time data provided by distributed measuring and control devices may be used in conjunction with current voltage regulation algorithms to improve regulation performance and reduce conservatism in the controller settings. See Figures 5, 6 and 7 for preliminary results.

Figure 6: Simulated voltages with present voltage regulation scheme under increased load. Violation of minimum voltage regulation limits (0.94 per unit) occur around times 1100, 1900, 2500 and 4000.

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Figure 5: Schematic of voltage regulation illustrating use of on load tap changing transformers (T1 and T2) and remote distributed monitoring and control sensors (DM&C).


Future work will investigate the coordination between on-load tap-changing transformers between the sub-transmission and distribution substations, to improve voltage stability and compensate perturbations induced by distributed generation. Advanced control schemes, such as model predictive control (MPC), in which voltage regulation is formulated as a constrained control design problem, can optimally account for the limited ranges of actuation and quantised output characteristic of on-load tap-changing transformers, while integrating multivariable information from distributed sensors.

A.5.2 Partial Discharge Localisation in Power Transformers

Researchers: Steve Mitchell James Welsh Jose De Doná

A Partial Discharge (PD) as its name implies, is the partial breakdown of an insulation barrier within a system resulting in an exchange of charge. Partial discharges over a period of time, are not only the cause of insulation damage, but are also useful in condition monitoring of insulation health. The measured magnitude of a PD is an indicator of the severity of the fault.

Figure 7: Simulated voltages with present voltage regulation scheme modified to use remote DM&C data when violations in minimum voltage level may occur. The proposed scheme handles the drop in voltage due to increased load by temporarily using the measured data to trigger tap changes, and then returning to the standard controller scheme once the regulation performance is restored.

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Commercial partial discharge monitoring tools typically measure the magnitude, frequency of occurrence, and the relative phase position at which they occur. However these measurements can be misleading due to the fact that, electrically, they can only be observed outside of the winding of the power transformer. The level of attenuation that the partial discharge undergoes during its passage through the winding can vary widely due to its dependence upon the source location of the PD. Therefore, an estimate of the location of the source, based on an accurate transformer model, would be a very useful tool for not only more accurate PD magnitude estimates, but also for transformer maintenance and repair.

A partial discharge can be considered to be a brief exchange of charge within the insulation system. This discharge can be modelled as an ideal current impulse. By determining the frequency response of the measured partial discharge, an estimation of the transfer function between the PD location and the measurement point is obtained. Incorporating this with an accurate model of the system will provide an estimate of the partial discharge site of origin.

In addition to electrically monitored partial discharges, many other techniques exist, e.g. ultrasonic, acoustic and capacitive probe. However, these techniques are limited in that they only obtain a relative indication of the PD magnitude. The model based approach advocated in this research will enable a true PD magnitude estimate to be obtained.

It is believed that the techniques proposed in this research could also be adapted for use in PD location in other equipment, e.g. electrical cables and rotating machines.


A.6 MARINE AND AEROSPACE SYSTEMS

Marine and aerospace systems are designed to perform complex operations and missions. Vehicle motion control systems are a fundamental component, and even an enabling factor for these missions. The goal for this group of projects is to develop novels tools for guidance, navigation, and motion control of marine and aerospace vehicles with the aim of optimising reliability and vehicle performance.

Researchers: Tristan Perez Alejandro Donaire Pierre de Lamberterrie (Student) Gijs Hilhorst (Visiting Student, Uni. of Twente, The Netherlands)

External Collaborators: Paul Steinmann (Halcyon Int. Ltd Pty, Australia)

Robert Borret (CFW Hamilton Jet & Co., New Zealand)

Brendan Williams (Boeing Research and Technology Australia)

Svein Peder Berg (Offhore Simulator Centre, Norway)

Francis Valentinis (DSTO Maritime Platforms Division, Vic).

A.6.1 Control of Marine Vessels with Water Jets (CFW Hamilton Jet & Co. New Zealand)

Researchers: Tristan Perez Alejandro Donaire

External Collaborator: Robert Borret (CFW Hamilton Jet & Co, New Zealand)

In this project, we consider the problem of vessel motion control at low speeds using water jets. The project is part of an upgrade of the current control systems of CFW-Hamilton Jet & Co. In 2009, we designed a multi-mode control architecture, proposed experimental tests, and selected system identification techniques to augment the system with an auto-tuning capability.

In 2010, we investigated improving the accuracy of system identification methods used to obtain parameters for control tunning and wave filter design. For marine systems, the sampling period chosen for measurement and control implementation is normally very small compared to the dynamics of the vessel response. This results in discrete-time model approximations that are numerically ill-conditioned. Therefore from the point of view of parameter estimation, it is worth investigating the direct use of continuous-time models.

This practice can also simplify the implementation of the wave filter.

In particular in 2010, we looked at the issue of parameter estimation accuracy based on different continuous-time model parameterisations. Based on the improved parameter estimates, we further considered self-tuning model-based wave filter design.

A.6.2 Gyroscopic Stabilisation of Marine Platforms (Halcyon International, Australia)

Researchers: Tristan Perez

External Collaborator: Paul Steinmann (Halcyon International, Australia)

The use of gyroscopic effects for the roll stabilisation of marine structures was proposed over 100 years ago. This approach was very effective, but limited control and large sizes hindered further development. In recent years there has been significant interest in revitalising gyrostabilisers due to improvements in materials, bearings, and lubricants, which have contributed to fast spinning devices and size reduction. This project aims at supporting Halcyon’s development of high performance gyrostabilisers.

In our previous work, we proposed an adaptive precession torque controller, and a multi-mode control architecture with the aim of augmenting the system with a fault-tolerant capability. The new controller combines three different controllers that make use of varying levels of information related to vessel and gyro-actuator motion. In 2010, Halcyon completed a first sea trial using the adaptive controller developed by CDSC.

A.6.3 Control of Underwater Vehicle Dynamics

Researchers: Tristan Perez Alejandro Donaire Gjis Hilhorst (Visiting Student)

External Collaborator: Francis Valentinis (DSTO – Maritime Platforms Division).

In this project, we have investigated novel motion control strategies for underwater vehicles. We proposed a new design methodology for overactuated open-frame vehicle positioning controllers, which incorporate a control allocation function. The proposed control system uses a mapping that translates the actuator force constraint set to a set in the generalised force space (force for degrees of freedom being controlled.) This novel problem formulation allows the designer to consider a constrained motion control strategy with unconstrained control allocation. Hence, the computational complexity of the controller is significantly reduced. We have also been able to demonstrate stability of closed loop control system analytically, and this has lead to closed form control tuning rules for robust performance.


We have also studied the robustness of the controller. Given the physical nature of the controlled system, we took an energy-based approach for designing a motion control system based on Port Hamiltonian System Theory. This allows a control design that ensures passivity, and under certain conditions stability. The advantage of designing a controller with stability properties depending on passivity is that parametric uncertainty and even changes in model structure may be tolerated provided the system remains passive. This is a very important feature for control design of underwater marine platforms due to the large parametric and model structure uncertainty.

A.6.4 Ship Motion Control for Offshore Marine Operations in Training Simulators (Offshore Simulator Centre AS, Norway)

Researchers: Tristan Perez Alejandro Donaire

External Collaborator: Sven Peder-Berge (Offshore Simulator Centre AS, Norway.)

Ship training simulators are used to enhance crew efficiency and thus improve the safety of marine operations. At the core of any virtual-reality simulator lays a mathematical model that describes the ship dynamic response to control and environmental forces. When a new vessel is to be incorporated into a simulator, different types of data of the vessel may be available to be used for system identification to extract a mathematical model.

In our previous work, we evaluated various identification methods for obtaining parametric models of vessel response based on frequency domain data computed by hydrodynamic codes, and made recommendations to the Offshore Simulator Centre as to which method to use for rapid identification of vessel dynamic response. We then developed a position hold controller with a novel adaptive wave filter capability. CDSC personnel have lodged a provisional patent application of an enhanced version of the wave filter, which can be used in real control implementation of dynamic positioning of offshore vessels. In 2010, we performed a study on simplified control allocation techniques, and the Offshore Simulator Centre successfully implemented the dynamic positioning controller into its simulator product.

A.6.5 Evaluation of Robust Autonomy for Uninhabited Airborne Systems (UAS) (Boeing Research & Technology Australia)

Researchers: Tristan Perez Alejandro Donaire, Pierre de Lamberterrie (Student)

External Collaborator: Brendan Williams (BR&TA)

Robust Autonomy (RA) for uninhabited airborne systems (UAS) refers to the ability of a vehicle to either continue operating in the presence of faults or safely shut down. To achieve this characteristic, a UAS has to incorporate mechanisms that augment the safety and reliability of its guidance, navigation, communication, and control (GNCC) systems. This project is aimed at developing various measures of performance and methods that would yield a figure of merit to assess robust autonomy. This figure of merit is related to the reliability of the fault-tolerant-control system. The purpose of the figure is evaluation of UAS flight control and guidance system performance in terms of simulation scenarios under the presence of faults and environmental conditions relevant to the mission of the UAS.

In past work we proposed a methodology to specify the mission-specific requirements for fault-tolerant GNCC system of UAS. We also proposed a method to quantify robust autonomy of UAS. Based on mission-vehicle specific performance criteria, we define a utility function, which can be evaluated using simulation scenarios for an envelope of environmental conditions. The result of these evaluations is a figure of merit for operational effectiveness. The procedure was then augmented to consider faults. Thus leads to a figure of merit related to robust autonomy. The proposed figures of merit are interpreted within a probability framework. The objective of these figures of merit is to use them at both vehicle development stage and with hardware-in-the-loop testing at certification stage. In addition, performance indices based on dynamic and geometric tasks associated with vehicle manoeuvring problems were proposed, and an example of a two-dimensional fly scenario was considered to illustrate the use of the proposed evaluation methods.

In 2010, we concentrated on the development of a simulator to test the use of the proposed figures of merit. This included the simulation of the aircraft and its environment, as well as an initial control design for way-point tracking.

We are currently looking at the design of a fault-tolerant flight control system to be implemented in the simulator and tested against a specific set of mission criteria.


A.6.6 Kinetic Modelling and Motion Control of Parallel Robotic Manipulators for Improved Automated Manufacturing (Boeing Aerostructures, Australia)

Researcher: Tristan Perez

External Collaborator: P. Crothers (Boeing Research and Technology Australia)

The use of parallel robotic manipulators is increasing in automated manufacturing. This type of manipulator is based on close kinematic chains (sequence of interconnected rigid links that form a loop). This configuration increases the structural stiffness of the robot and reduces energy consumption at the expense of a richer kinematic model geometry and more challenging motion control design. In 2010, we identified a number of open problems in kinetic modelling and motion control of a particular type of parallel kinematic manipulator. The solution of these problems are expected to result in an improved time response and accuracy of these manipulators. The work will continue in 2011.


A.7 VIRTUAL LABORATORIES FOR CONTROL ENGINEERING EDUCATION

Researchers: Graham Goodwin David Ferris Adrian Medioli James S. Welsh

External Collaborators: Ljubo Vlacic (Griffith University, Australia) Willy Sher (University of Newcastle, Australia)

We have continued our work on emulation-based virtual laboratories in control engineering education. We believe that such emulation experiments can give students an industrially-relevant educational experience at relatively low cost. Recent work has focused on development of a Virtual Laboratory for Control of a Wind Energy System and a Bioreactor. See G.C. Goodwin, A.M. Medioli, W. Sher, L.Vlacic and J.S. Welsh, in Journal Publications.

We have also recently signed an agreement with quanser Inc. to become an exclusive marketing agent for the Virtual Laboratory system.


A.8 MULTIOBJECTIVE OPTIMISATION FOR BUILDING COMFORT AND ENERGY

Researchers: Julio Braslavsky Cristian Perfumo (Student) John Ward (CSIRO)

This project deals with the modelling, simulation and control of the aggregated power demand of populations of heat, ventilating and air conditioning (HVAC) devices. An HVAC device is typically controlled using a thermostat and an on-off actuator with hysteresis and operates on a duty cycle that regulates temperature within the desired ranges of comfort. Large population of HVAC devices, despite being independently controlled, are prone to synchronise their operation under abrupt changes in weather or energy supply, which generates undesirable slow transient response and large peaks in their aggregated power demand.

The project aims to develop methods to prevent, or at least, minimise such undesirable responses by assuming remote manipulation of the temperature setpoints of the population of HVAC devices, while maintaining acceptable levels of comfort and infrastructure costs. In 2010, this project has progressed by developing a stochastic dynamic model of populations of independently regulated HVAC devices. This stochastic model has large complexity and, while it accurately describes the dynamics of the aggregated power demand response of the population, it is not amenable to control design. A preliminary simplified linearised model has been developed to capture small increment dynamics, which will be used to design a supervisory control strategy to manipulate small changes in temperature setpoints. In addition, these models will be used to quantify fundamental limitations to the manipulation of the dynamic response of the aggregated power demand of HVAC devices to track profiles as shown in Figure 8.

Figure 8: Potential scenario for power demand tracking, in which aggregated load is reduced (and temperature setpoint increased) by allowing pre and post cooling periods. This scenario models a typical desired response that aims to minimize power demand during peak time periods.


Program Goals:

Many technical processes and products in the area of mechanical and electrical engineering show an increasing integration of mechanics with electronics and information processing. This integration is between the components (hardware) and the information-driven functions (software), resulting in integrated systems called mechatronic systems. The development of mechatronic systems involves finding an optimal balance between the basic mechanical structure, sensor and actuator implementation, automatic digital information processing and overall control, and this synergy results in innovative solutions. The practice of mechatronics requires multidisciplinary expertise across a range of disciplines such as: mechanical engineering, electronics, information technology, and decision-making theories. These complicated interactions generate a rich and complex set of dynamic behaviours to be analysed and controlled. This Program is aimed at investigating such analysis and control questions in emerging mechatronic systems.

B.1 A MICROMACHINED 2DOF NANOPOSITIONER WITH INTEGRATED CAPACITIVE DISPLACEMENT SENSING

Researchers: Lujun Ji Yong Zhu S.O. Reza Moheimani Mehmet Yuce

A micro-electro-mechanical system (MEMS) electrostatic stage capable of two degree-of-freedom (2DOF) positioning with motion ranges of micrometers has been designed and built. The main emphasis in this area has been on development of various actuation principles. However, without sensing, nanopositioning with closed-loop feedback control cannot be implemented to achieve important objectives such as improved dynamic behaviour of the actuator with fast response time, precise position control and continuous tuning of position.

This project reports the design, fabrication and characterization of a MEMS-based 2DOF nanopositioner. The proposed nanopositioner, consisting of comb-drive actuators and on-chip capacitive displacement sensors in both X – and Y-directions, can simultaneously actuate the micro-stage and sense the corresponding displacements. A commercial capacitive readout IC (MS3110) is used for open-loop capacitive sensing. The developed electrostatic nanopositioning stage can be controlled automatically with a closed-loop controller. The MEMS device was fabricated by following the standard SOIMUMPs process at MEMSCAP. The positioner has a size of 3.75 x 3.75mm2 with dynamic ranges from -5.95 µm to +5.86 µm and from +5.89 µm to +5.90µm along the X – and Y-directions respectively with an actuation voltage of up to 100 V. The first resonance frequencies for the X – and Y-directions of the stage are measured to be 3.641 kHz and 4.422 kHz respectively. The designed structures are proved to achieve highly decoupled motions with measured -35 dB of cross-coupling between the X – and Y-directions.

B. MEChATRoNICS

S.O. Reza Moheimani Program Leader

Andrew Fleming Deputy Program Leader


Figure 9: SEM image of the micromachined 2DOF nanopositioner. (a) whole structure; (b) magnified view (centre stage).

Figure 10: Measured frequency responses of the 2DOF nanopositioner, both magnitude (upper trace) and phase (lower trace). (a) X-direction. (b) Y-direction.


B.2 DESIGN, CHARACTERIZATION, AND CONTROL OF A MICROMACHINED NANOPOSITIONER WITH ON-CHIP ELECTROTHERMAL ACTUATION AND SENSING

Researchers: Yong Zhu Ali Bazaei S.O. Reza Moheimani Mehmet Yuce

High precision nanopositioners have been used extensively in many applications such as scanning probe microscopy (SPM), atomic force microscopy (AFM), and data storage. Closed-loop feedback control of the positioners is highly desirable for high degrees of displacement precision, and needs an accurate source of position information. However, the in-plane movements are mostly measured by laser reflectance microscopy, making the footprint of the system relatively large. In this project, a novel thermal position sensor has been integrated with a thermal actuator in the same MEMS chip.

The displacement information of the positioner stage can be detected by measuring the resistance difference between the two sensors. The differential changes of the resistance result in current variations in the beam resistors, and the currents are converted to an output voltage using the trans-impedance amplifiers and an instrumentation amplifier. To suppress the common-mode noise, the gains of these two trans-impedance amplifiers must be well matched. Employing the differential topology allows the sensor output to be immune from undesirable drift effects due to changes in ambient temperature or aging effects.

The nanopositioner was calibrated using a PolytecTM Planar Motion Analyzer (PMA). With the actuation voltage of 9 V, the thermal actuator can achieve a maximum displacement of 14.4 µm. Meanwhile, at every actuation voltage, the instrumentation amplifier outputs were recorded for calibration of the position sensors. The sensors were biased with 6 V, and the instrumentation amplifier gain was set at 90.3 V/V. At this bias voltage, the sensors have a power consumption of 120 mW and a sensitivity of 0.27 mV/nm. The frequency response shows the open-loop bandwidth of the positioner is 101Hz. Thanks to the differential sensing of the sensor pair, the open-loop amplifier output has a low drift of 2.4 mV over 2000 seconds, which corresponds to 8.9 nm displacement. Proportional-integral (PI) closed-loop feedback control of the developed positioner was investigated to improve positioning accuracy and robustness of the system. A controllable desired response of 2.5 µm steps over a 10 µm range was obtained with a positioning resolution of 7.9 nm and a time constant of 1.6 ms. As a comparison, a similar open-loop seek operation resulted in a maximum positioning error of 0.62 µm.

Figure 11: SEM Image of the micromachined nanopositioner with on-chip electrothermal actuator and position sensor.

Figure 12: Closed – and open-loop experimental results for 2.5, 5, 7.5, and 10 µm seek operations.


B.3 HIGH-SPEED CYCLOID-SCAN ATOMIC FORCE MICROSCOPY

Researchers: S.O. Reza Moheimani Yuen Kuan Yong Ian R. Petersen (ADFA, University of NSW)

A key hurdle in achieving high scan speeds in atomic force microscopes is that the probe is required to be scanned over the sample in a zig-zag raster pattern. The fast axis of the AFM scanner must track a signal that contains frequencies beyond its mechanical bandwidth. Consequently, fast raster scans generate distortions in the resulting image. We propose a smooth cycloid-like scan pattern that allows us to achieve scan speeds much higher than a raster scan. We illustrate how the proposed method can be implemented on a commercial AFM with minimal modifications. The electrodes are arranged such that the tube is driven in an anti-symmetrical manner, resulting in a collocated system suitable for positive position feedback (PPF). A PPF controller is designed to damp the scanner’s resonance. Piezoelectric strain-induced voltage is used as measurement.

The proposed scan trajectory is plotted in Figure 13 and is characterized by the following equations: x(t) = at + r sinwt,y(t) = r cos wt

Figure 13: Scan images in 2D and 3D together with their single line Z-direction profiles of the grating. Images of a 10 ? 20um area were recorded at 1.95 Hz (256 ? 512 lines), 39.1 Hz (256 ? 512 lines), 78.1Hz (256 ? 512 lines) and 156.25Hz (128 ? 256 lines).

where w = 2p f and f is the scan frequency, r is the amplitude of the input waveforms and w is the ramp rate of the x input signal. The significance of this method is that it does not require specialized apparatus to develop high-quality images at very high scan speeds and it works quite satisfactorily without the need to dampen vibratory modes of the scanner, which is a necessity in high-speed raster scan AFMs.

The cycloid-scan method was implemented on a commercial AFM (NT-MDT NTEGRA). To evaluate the effectiveness of our proposed scan method, we used a MikroMasch TFq1 calibration grating with 3 mm period, 1.5 mm square side and 20nm height. The resonance frequency of the piezoelectric tube scanner used in the experiments was approximately 580 Hz in both X and Y axes. The scanner was operated in open loop. Images of a 10 x 20 mm area of the grating were recorded at 1.95 Hz (256 x 512 lines), 39.1 Hz (256 x 512 lines), 78.1 Hz (256 x 512 lines) and 156.25 Hz (128 x 256 lines). The figure below shows the scan images of the grating together with their cross-section profiles. For raster scans, scan-induced oscillations were observed at 39.1, 78.1 and 156.25 Hz. For cycloid scans, images recorded at 39.1 and 78.1 Hz have similar quality as that obtained at 1.95 Hz. Scan-induced oscillations were not noticeable in these images, indicating that the cycloid-scan input signals did not excite the vibratory dynamics of the tube scanner. The image quality at 156.25 Hz was slightly lower than that of the other three cycloid-scanned images. This is expected as the quality of a scan will deteriorate with the increase of scan speed due to the oscillations of the cantilever. Nevertheless, the image quality is much superior to the raster-scanned images.


Figure 14: Left: CAD drawings of the XYZ scanner. (a) Assembly view. (b) Exploded view. Right: Raster-scanned images of the XYZ scanner.

B.4 A COMPACT XYZ SCANNER FOR HIGH-SPEED ATOMIC FORCE MICROSCOPY

Researchers: S.O. Reza Moheimani Yuen Kuan Yong

A fast AFM scanner has been designed for high resolution imaging. The main goals are to increase the scanner resonance frequencies in X, Y and Z axes and to minimize the cross-coupling among the three scan axes. The proposed AFM scanner consists of a XY nanopositioner, a Z nanopositioner, a base, three piezoelectric stack actuators, two capacitive sensors and a sensor

target. The assembly and exploded views of the scanner are shown in Figure 14 below. The scanner is designed to fit into a commercial NT-MDT NTEGRA SPM. This SPM is designed around a piezoelectric tube nanopositioner. In order to install the flexure-based scanner into the small designated space, the scanner was designed to be very compact, which is 50mm in width.

The first resonance frequency appears at 10kHz in the X, Y and Z axes. The measured cross-coupling is about -35dB, i.e. parasitic motions of the non-actuated axes are 1.8% of the actuated axis.

The scanner was installed in the AFM to obtain images of a MikroMasch TDG01 calibration grating. The grating has parallel ridges with a 278nm period and approximately 55nm height. Images of the grating were recorded in constant force contact mode at 10Hz (256x256 lines), 200Hz (200x200 lines) and 312.5Hz (128x128 lines). Due to the bandwidth limitation of the data acquisition system, the scan lines had to be reduced as the scan rate was increased. Figure 14 below shows the 3.5 mx3.5 m images of the grating together with their X and Z axis time signals. Oscillations were not noticeable in the image as well as time signal.


In Figure 15 (a), a piezoelectric actuator is pictured with an integrated monolithic force sensor and strain gage. The tracking performance in (b) demonstrates the excellent high-speed positioning accuracy.

B.5 INTEGRATED STRAIN AND FORCE FEEDBACK FOR HIGH PERFORMANCE CONTROL OF PIEZOELECTRIC ACTUATORS

Researchers: Andrew Fleming K.K. Leang (Mechanical Engineering, University of Nevada, Reno)

Due to their high stiffness, compact size and effectively infinite resolution, piezoelectric actuators are universally employed in a wide range of scientific and industrial applications, such as nanofabrication systems, fibre aligners, beam scanners, and scanning probe microscopes. Unfortunately, piezoelectric actuators cannot be directly applied in positioning applications as they exhibit a significant amount of hysteresis over large ranges and creep at low frequencies. These effects can cause tracking error in excess of 20%. As a result, many applications requires some form of feedback or feedforward control to reduce or eliminate nonlinearity.

This project proposes a new high-performance sensor and feedback arrangement for controlling piezoelectric actuators. A piezoelectric force sensor is combined with a resistive strain gage to provide both extremely low noise and high stability. The use of a force sensor also results in a system transfer function that exhibits zero-pole ordering. Such systems allow a simple integral controller to provide excellent tracking and damping performance with guaranteed stability.

The proposed technique was demonstrated on a nanopositioning platform with a range of 10um and a resonance frequency of 2.4 kHz. In closed-loop, the controller damps the resonance by 33 dB and increases the tracking bandwidth from 210 Hz to 2.07 kHz. With a range of 10 microns, the worst-case resolution is 670 picometers, or around 6 atomic diameters.

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B.6 DUAL-STAGE VERTICAL FEEDBACK FOR HIGH-SPEED SCANNING PROBE MICROSCOPY

Researcher: Andrew Fleming

Many popular modes of scanning probe microscopy require a vertical feedback system to regulate the tip-sample interaction. Examples include constant-current scanning tunneling microscopy and constant-force atomic force microscopy. Due to the control of tip-sample interaction, these modes of microscopy provide precise topographic information and result in drastically reduced sample damage, hence their popularity. Unfortunately the vertical feedback controller also imposes a severe limit on the scan speed of scanning probe microscopes.

In this project, the foremost bandwidth limitation is identified to be the low-frequency mechanical resonance of the scanner. To overcome this limitation, a dual-stage vertical positioner is proposed. This comprises the original scanner, plus an additional high-speed stage. The improved bandwidth provided by the high-speed stage allows a vast improvement in bandwidth from 83 Hz to 2.7 kHz. This improvement allows image quality to be retained with a speed increase of 33 times, or alternatively, feedback error can be reduced by 33 times if scan speed is not increased. The techniques proposed are mechanically and electrically simple and can be retrofitted to any scanning probe microscope.

Figure 17: Due to the increased feedback bandwidth, the imaging performance with dual-stage control positioning demonstrates a greatly improved resolution.

Figure 16: The high-speed vertical positioned (left), mounted on the microscope base with an attached sample (right).


B.7 BRIDGING THE GAP BETWEEN CONVENTIONAL AND VIDEO-SPEED SCANNING PROBE MICROSCOPES

Researchers: Andrew Fleming K.K. Leang (Mechanical Engineering, University of Nevada, Reno) B.J. Kenton (Mechanical Engineering, University of Nevada, Reno)

In addition to topographic imaging, the Scanning Probe Microscope (SPM) has provided a window into the operation of physical interactions at the micro – and nano-scale. Due to the unique imaging capabilities and wide range of use, there has been a dramatic increase in the number of imaging modes and sensors since the invention of the Atomic Force Microscope (AFM) in 1986. However, in this same period, the speed of standard scanning probe microscopes has not kept pace with other aspects of the technology. For example, with a typical scan-rate of 1 Hz, a single image may take minutes to acquire. In many applications, this lengthy imaging time is simply an inconvenience; however, in other applications, the low speed becomes a critical limitation.

There are four major factors that limit the speed of scanning probe microscopes. These are: the resonance frequency or bandwidth of the probe; the resonance frequency or bandwidth of the scanner; the bandwidth of the acquisition system; and finally, the closed-loop bandwidth of the vertical feedback controller.

This project proposes three new techniques that can be combined to eliminate the above limitations and increase the speed of a conventional scanning probe microscope by greater than one hundred times. This is achieved by the combination of high-speed vertical positioning, sinusoidal scanning, and high-speed image acquisition. These techniques are simple, low-cost, and can be applied to many conventional microscopes without significant modification. Experimental results demonstrate an increased scan rate from 1 Hz to 200 Hz. This reduces the acquisition time for a typical image from 3 minutes to 1 second.

With sinusoidal scanning and high-speed vertical positioning, the image acquisition time can be reduced from 3 minutes to 1 second, without sacrificing image quality.

Figure 19: The top two images demonstrate the typical artefacts that can occur with a 10Hz line rate. In the top image, the vertical feedback bandwidth is insufficient. In the middle image, the scanner vibration distorts the image. The bottom image demonstrates what can be achieved with sinusoidal scanning and high-speed vertical positioning. These techniques enable the line-rate to be increased to 200Hz without sacrificing imaging quality. This reduces the image acquisition time from 3 minutes to 1 second.

Figure 18: The high-speed vertical positioning system with inertial cancellation. This system has a first resonance frequency of 103 kHz which is 800 times faster than a standard AFM positioner


B.8 ROBUST CONTROL OF SPM WITH SIGNAL TRANSFORMATION AND LIMITED NOISE BANDWIDTH

Researchers: Ali Bazaei Yuen Kuan Yong S.O. Reza Moheimani

Accurate tracking of a fast triangular waveform is one of the major challenges in scanning probe microscopy and other scanner-based devices such as optical scanners. Raster scanning is also used in emerging probe-based data storage devices that demand high speed positioning with limited control bandwidth, due to measurement noise, actuator limitations, etc. Hence, feedback control methods with limited closed-loop bandwidth are of considerable importance.

For an acceptable tracking, the closed-loop bandwidth of an ordinary one-degree-of-freedom (1-DoF) feedback system should be at least three times more than the fundamental frequency of the triangular waveform.

Signal transformation approach, shown in Figure 20, incorporates appropriate mappings f and f–1 between nonsmooth signals (e.g. triangular waveforms) and smooth signals (e.g. ramps) in a control system to improve the tracking error while keeping the closed-loop bandwidth low to limit the projected measurement noise. However, the method is not robust to DC gain variations of the plant and output disturbances.

A remedy for the mentioned problem is to incorporate a robustification loop before using signal transformation blocks, as shown in Figure 21 for x-axis of a SPM scanner with a piezoelectric tube stage. The inner loop in Figure 21 uses a low noise piezoelectric strain signal vpx to damp the first resonance of the tube. In the middle loop, a low-pass filter F(s) limits the closed-loop noise bandwidth and an integrator compensator Ki(s) provides a robust unity DC gain from u to scanner displacement x as well as output disturbance ejection. Such robust performances are preserved even after incorporating the signal transformation blocks and designing a double integrator controller Kii(s). Simulations and experiments show that the root-mean-square of the tracking error with the proposed method is almost three times lessthan that of the ordinary a 1-DoF feedback system with the same limited closed-loop noise bandwidth.

Results of implementations of the proposed robust signal transformation method and an ordinary feedback system on the x-axis of the SPM scanner are shown in Figure 22. To capture pictures from a calibrated grating sample with periodic square pattern, a triangular signal was selected as a reference for x-axis and a ramp signal for the y-axis. The scanner is also under the influence of output disturbances due to the cross-coupling between the two axes. Both proposed and ordinary controllers were tuned to keep low bandwidths and almost the same standard deviation of 0.13nm for the projected noise of x-axis. The results show that incorporation of signal transformation can provide considerable improvements in the tracking performance and picture quality compared to the ordinary feedback controller.

Figure 21: Incorporation of robustification loop for x-axis of a tube-scanner to improve robust performance of signal transformation method.

Figure 22: Experimental comparison results for robust signal transformation and ordinary feedback systems, under the constraint of having similar standard deviations for the projected measurement noise. The dashed and solid lines in (a) and (b) refer to reference and controlled x-positions, respectively, (c) and (d) show the resulting AFM images (10µm×10µm) using robust signal transformation and the ordinary feedback system, respectively.

Figure 20: Incorporation of signal transformation blocks into a 1-DoF feedback system to improve tracking performance of a triangular reference.

Robust Signal Transformation Ordinary Feedback Control


B.9 TRACKING CONTROL OF ARBITRARILY SHAPED REFERENCES USING SIGNAL TRANSFORMATION

Researchers: Ali Bazaei S.O. Reza Moheimani

Low bandwidth feedback control is of significance importance for precision control of plants in the presence of measurement noise. Signal transformation is a novel control method which was originally developed for tracking of triangular reference signals. The method can introduce significant tracking improvement with limited closed-loop bandwidth.

This work is the first attempt to extend the signal transformation method for tracking of arbitrarily shaped reference signals. To do this we assume that the reference signal is continuous, pre-determined, and can be partitioned into time intervals within them it behaves monotonically. Figure 23 shows the proposed signal transformation method, where f and f–1 refer to mappings that can transform the reference signal to a ramp signal and vice versa, respectively. As the profile of the reference signal, which is used in the signal transformation blocks, is generally a nonlinear function of time, the closed-loop system has a nonlinear behavior in each monotonic time interval of the reference. Moreover, the nonlinear behavior is switched to another one when the reference signal switches from one monotonic time interval to the next one.

Hence, the closed loop system is a switched nonlinear system, whose analysis and performance is investigated in this work.

Figure 24 (a) shows the simulation results associated with the tracking of an arbitrarily shaped reference signal for a unity DC gain non-minimum-phase stable plant, when the proposed signal transformation method is incorporated into an ordinary feedback system with a low bandwidth of 0.88Hz. Clearly, the proposed method has significantly improved the tracking performance of control system compared to that of the ordinary feedback system. As the plant is stable and has a unity DC gain, a comparison of tracking errors between the closed-loop system with signal transformation and the openloop plant is also shown in Figure 24 (b). Clearly, performance of the proposed method, which has a bandwidth of 0.88Hz, is still better than that of open-loop plant with a 42Hz bandwidth.

Figure 23: Incorporation of signal transformation blocks into a 1-DoF feedback system to improve tracking performance of an arbitrarily shaped reference signal xd.

Figure 24:

(a) Comparison of controlled outputs when signal transformationblocks are included (solid line) and not included (blue dashed line) in the control system.

(b) A comparison between the tracking errors of controlled output by signal transformation and open-loop methods.

(a) (b)


C. CoNTRoL SySTEM DESIgN

Program Goals:

The goal of this program is to develop new techniques for analysis and design of complex control systems. Of special interest are nonlinear and nonsmooth behaviour, high state dimension and lack of convexity.

C.1 ROBUST MODEL PREDICTIVE CONTROL

Researchers: Graham Goodwin Maria Seron Christian Lovaas (Statoil, Norway)

In our previous annual report we described a new robust output-feedback model predictive control (MPC) design for a class of square, open-loop stable systems, having hard constraints, linear-time invariant model uncertainty and non-vanishing output disturbances. The proposed robust MPC design is based on a single quadratic programme (qP) and a linear estimator utilising a standard output disturbance model in order to estimate both the output disturbance and the system state. In order to ensure robust closed-loop stability whenever the qP is feasible at the initial time, “tighter” constraints on the predictions are imposed, and the cost function parameters are chosen off-line so as to satisfy a linear matrix inequality condition. This year the results have been published in the journal paper Lovaas, Seron and Goodwin, 2010.

C.2 CONSTRAINED MOTION PLANNING

Researchers: Jose De Doná Maria Seron Fajar Suryawan (Student)

This project has focused on the problem of trajectory planning for flat systems with constraints. Flat systems have the useful property that the input and the state can be completely characterised by the so-called flat output. We have proposed a spline parameterisation for the flat output, the performance output, the states, and the inputs.

Using this parameterisation the problem of constrained trajectory planning can be cast into a simple quadratic programming problem. An important result is that the B-spline parametrisation used gives exact results for constrained linear continuous-time system. The result is exact in the sense that the constrained signal can be made arbitrarily close to the boundary without having intersampling issues (as one would have in sampled-data systems). We have obtained experimental results where two methods to generate trajectories for a magnetic levitation (Maglev) system in the presence of constraints are compared. The first method uses the nonlinear model of the plant, which turns out to belong to the class of flat systems. The second method uses a linearised version of the plant model around an operating point. In every case, a continuous-time description is used. The experimental results on the real Maglev system show that, in most scenarios, the nonlinear and linearised model produce almost similar, indistinguishable trajectories. The results have been reported in a journal paper by Suryawan, De Doná and Seron, and in two conference papers (Suryawan, De Doná and Seron, Mediterranean Control Conference 2010).

Maria Seron Program Leader

Graham Goodwin Deputy Program Leader


C.3 FAULT TOLERANT CONTROL

Researchers: Maria Seron Jose De Doná Jan Richter (Siemens, Germany) Sorin Olaru and Cristina Stoica (SUPELEC, Paris, France) Monica Romero and Hernan Haimovich (University of Rosario, Argentina) John-Jairo Martinez (GIPSA-lab, ENSIEG, Grenoble, France) Alain Yetendje (Student) Raheleh Nazari (Student) Florin Stoican (Student SUPELEC) Matias Nacusse (Student University of Rosario, Argentina)

This project is part of a general project on fault tolerant control (FTC), co-funded by the ARC discovery grant DP0881419: Fault tolerant multisensor feedback control. Key elements of the developed FTC approach include the use of unknown but bounded disturbance models, the computation of ‘healthy’ (invariant) sets where the closed-loop system trajectories are confined under healthy operation, and the separation of these ‘healhty’ sets from ‘under-fault’ sets, where the trajectories move after changes in the system fault situation occur. The emphasis this year has been on improved strategies for dealing with actuator faults, fusion-based reconfiguration strategies after sensor faults, the adaptation of the techniques to deal with constraints, the extension to classes of switched and nonlinear systems, and the application of the proposed methodologies to various problems of practical interest.

C.3.1 Actuator Fault Tolerant Control

Journal versions of previously reported work that developed actuator FTC strategies both for continuous-time [Ocampo-Martinez et al., 2010] and discrete-time systems [Seron and De Doná, 2010] have been published this year. The work for discrete-time systems extends our previous results in this topic since it considers a larger class of faults by treating not only actuator outage but also loss of effectiveness by an uncertain amount. In addition, we have extended the multi-controller used for reconfiguration to incorporate integral action. This latter feature allowed us to successfully apply the method and achieve offset-free setpoint tracking under a range of fault situations for a nonlinear benchmark model consisting of two interconnected tanks, often utilised in the FTC literature.

C.3.2 Multisensor Fusion Fault Tolerant Control

In previous reports we described multisensor switching strategies for fault tolerant control, where estimates associated with multiple sensor outputs are switched based on an appropriate switching criterion. This year we have developed an analogous approach that employs fusion, rather than switching, of estimates. The strategy uses a fault detection and identification unit which verifies that, for each sensors-estimator combination, the estimation tracking errors lie inside pre-computed sets and discards faulty sensors when their associated estimation tracking errors leave the sets. The remaining healthy estimates are combined using a technique based on the optimal fusion criterion in the linear minimum-variance sense. The fused estimates are then used to implement a state feedback tracking controller. The scheme comes with pre-checkable conditions that guarantee closed-loop stability and performance under the occurrence of abrupt sensor faults. The results are reported in the accepted journal paper Yetendje, De Doná and Seron, 2010.


C.3.3 MPC-based Fault Tolerant Control of Constrained Multisensory Linear Systems

We have extended our previous FTC designs to consider also the very important practical issue caused by the presence of constraints in the frame of linear discrete-time systems subject to state and measurement disturbances. The proposed scheme includes a sensor FDI strategy which, as in our previous work, employs a bank of sensor-estimator combinations and achieves fault diagnosis based on a set-separation principle. To deal with constraints, we have integrated the FDI in an active FTC scheme based on the output feedback “tube” MPC controller of Mayne and co-authors. Indeed, the output feedback controller yields a “tube”, whose center is generated by using conventional MPC with tighter constraints on the nominal system, and whose size is restricted by using a local feedback that attempts to steer all trajectories of the uncertain system to the central trajectory. Proofs of fault tolerance of the resulting closed-loop system and robust exponential stability of a robust invariant set are provided under a set of conditions on the system parameters (disturbance bounds, reference offsets and bounds, etc.) in the fault-free case and under the occurrence of sensor faults. This work has also expanded the fault scenarios employed in our previous work to consider sensor loss of effectiveness by an unknown amount (including sensor outage) and the likely case of sensor bias. The results were communicated in the Systol’10 conference paper Yetendje, Seron and De Doná, 2010.

C.3.4 Fault Tolerant Control Based on Sensor-Actuator Channel Switching and Dwell Time

In this work we have proposed a switching control scheme for a plant with multiple estimator-controller-actuator pairs. The scheme has to deal with specific problems originated by the switching between the different feedback loops and accommodate faults in the observation channels (sensors outputs). The main contribution is a fault tolerant switching scheme with stability guarantees assured by a pre-imposed dwell-time. The detection and the fault tolerance capabilities are assured through set separation for the residual signals corresponding to healthy and faulty functioning. Another contribution of the work resides in a recovery technique for faulty sensors which makes use of a virtual sensor whose estimation, based on an optimisation procedure, minimises recovery time. The results are presented in the CDC’10 conference paper Stoican et al., 2010.

C.3.5 Reference Governor for Tracking with Fault Detection Capabilities for a Class of Nonlinear Systems

This project extends our previous work on fault tolerant multisensory switching control to a class of nonlinear systems. As in our previous work, the key point to ensure fault tolerance is the separation between healthy and faulty closed-loop behaviour. Here we achieve this through set theoretic operations upon sets describing the healthy/faulty behaviour of the system. The results rely both on an appropriate choice for the exogenous signals and on fixed point conditions for a nonlinear mapping which describes the gap between the nonlinear system and a linearised model in the functioning interval. A reference governor is employed such that, under a receding horizon technique, only feasible exogenous signals are provided to the system. The results were communicated in the Systol’10 conference paper Stoican et al., 2010.

C.3.6 Fault Tolerant Control Applications

We have successfully applied our fault tolerant control approach to simulation models of induction motor control, doubly-fed induction generator control and power electronics. Results were reported in the journal paper Romero, Seron and De Doná, 2010, in the two conference papers by Romero and Seron, 2010, and in the conference paper Nacusse et al, 2010.

We have also provided an experimental validation of the methodology through its implementation on a laboratory-scale magnetic levitation system. The results were published in the journal paper Yetendje, Seron, De Doná and Martinez, 2010, and in the conference paper Nazari, Yetendje and Seron, 2010.

C.4 INVARIANT SETS AND ULTIMATE BOUNDS IN PERTURBED SYSTEMS

Researchers: Maria Seron Jose De Doná

External Collaborators: Hernan Haimovich (University of Rosario, Argentina) Sorin Olaru (SUPELEC, Paris, France) Florin Stoican (Student SUPELEC, Paris, France)

In previous reports we presented a systematic method to compute invariant sets and ultimate bounds for perturbed systems. The method is based on a componentwise analysis of the system in modal coordinates and thus exploits the system geometry as well as the perturbation structure without requiring calculation of a Lyapunov function for the system. The method has been successfully applied to the analysis of sampled-data systems with quantisation and to the development of a new robust controller design method. This year,


results of the extension of the method to switched linear systems with disturbances and arbitrary switching were published in the journal paper Haimovich and Seron, Automatica, 2010. The proposed extension relies on the existence of a transformation that takes all matrices of the switched linear system into a convenient form satisfying certain properties. We have shown that the transformation required by our method can easily be found in the well-known case where the subsystem matrices generate a solvable Lie algebra, and we have provided a systematic method to seek such transformation in the general case.

We have also extensively used the systematic ultimate bound computation method in our set-based approach to fault tolerant control, as described in C.3 above. In particular, we have developed a contractive procedure which, starting from an invariant set computed using the ultimate-bound method, iteratively refines this set to obtain arbitrarily close approximations of minimal robust positive invariant sets for linear systems. Contractive procedures are proposed and the cases of switching between different sets of disturbances and the inclusion of a predefined region of the state space are treated in detail. The resulting sets are used in multisensor control schemes which have to deal with specific problems originated by the switching between different estimators and by the presence of faults in some of the sensors. We have reported these findings in the accepted journal paper Olaru, De Doná, Seron and Stoican, 2010.

C.5 APPLICATION OF ADVANCED CONTROL TO POWER ELECTRONICS AND DRIVES

Researchers: Graham C. Goodwin Colin Coates Galina Mirzaeva Daniel E. quevedo

External Collaborators: David q. Mayne (Imperial College, London) Keng-Yuan Chen (National Chiao Tung University, Taiwan)

Control technology underpins the operation of many, and arguably all, modern high technology systems. Such systems include transportation (aircraft, high speed trains, marine vessels, automobiles), telecommunication systems, electricity networks, mining, minerals processing and agriculture. A particular area where control is playing an increasingly important role is industrial electronics. In this research program we have adopted a control engineering perspective. Our final goal is to find common ground between traditional control methodologies such as PCM and Hysteresis based methods and modern strategies such as Model Predictive Control. See G.C. Goodwin, D.q. Mayne, T. Chen, C. Coates, G. Mirzaeva and D.E. quevedo, in Plenary Papers; M. Cea, G.C. Goodwin and A. Feuer; G.C. Goodwin, M. Cea and A. Feuer, in Conference Papers.

C.6 DESIGN OF NETWORKED CONTROL SYSTEMS USING THE ADDITIVE NOISE MODEL METHODOLOGY

Researchers: Graham Goodwin Eduardo Silva (UTFSM, Chile) Daniel quevedo

Networked Control has emerged in recent years as a new and exciting area in systems science. The topic has many potential applications in diverse areas ranging from control of microrobots to biological and economic systems. The supporting theory is very rich and combines aspects of control, signal processing, telecommunications and information theory. Our research has emphasized the additive noise model methodology. See E.I. Silva, G.C. Goodwin and D.E. quevedo; G.C. Goodwin, E.I. Silva and D.E. quevedo, in Journal Publications.

C.7 qUANTIZED MODEL PREDICTIVE CONTROL WITH HORIZON ONE?

Researchers: Claus Müller Daniel quevedo Graham Goodwin

Model Predictive Control is increasingly being used in areas where decision variables are constrained to finite or countable infinite sets. Well known fields include Power Electronics, Signal Processing, and Telecommunications. Typically, the applications utilize high speed sampling and, thus, there is an incentive to reduce computational burden. One way of achieving this is to use small optimization horizons. This raises the question as to the optimality and performance of control laws with short horizons. In this research program we have developed necessary and sufficient conditions for horizon one quantized model predictive control to be equivalent to the use of larger horizons. We have also explored situations where horizon one is near optimal. See C. Müller, D.E. quevedo and G.C. Goodwin, in Journal Publications.


D. SIgNAL pRoCESSINg

In 2010, we continue to make major progress in the following research areas:n networked control systems;n system identification;n communications;n filter design and speech analysis;n dual-stage actuator systems.

The first area studies control and estimation problems for networked dynamic systems. This year’s main effort has been on new research initiatives in relation to smart electricity grids, in collaboration with EnergyAustralia and research partners in China. Our main focus has been on communication network development and control issues for power distribution networks. In addition, we have started working on state estimation for smart electricity networks. The second research area studies system identification problems subject to communication constraints, including quantization effects, packet dropout and network delays. The third area represents a continuing collaborative project with Ericsson, Sweden in the field of wireless communications. The fourth area studies new filter design methods for speech signal processing. New progress has been made on efficient sound synthesis and vocal track model estimation. The fifth area studies advanced control methods for high-precision, high-performance control actuators using a dual-stage actuator structure.

D.1 IDENTIFICATION OF LINEAR SYSTEMS SUBJECT TO COMMUNICATION CONSTRAINTS

Researchers: Damian Marelli Minyue Fu

We study system identification of linear models whose outputs are subject to finite-level quantization and random packet dropouts. Using the maximum likelihood criterion, we propose a recursive identification algorithm, which we show to be strongly consistent and asymptotically efficient. We also propose a simple adaptive quantization scheme, and we show that it asymptotically achieves the minimum parameter estimation error covariance. The joint effects of finite-level quantization and random packet dropouts on identification accuracy are exactly quantified, and our theoretic results are verified by numeric experiments.

D.2 HIGH-SPEED ANALOG-TO-DIGITAL CONVERTER DESIGN

Researchers: Damian Marelli Minyue Fu Kaushik Mahata

The hybrid filter bank architecture permits implementing accurate, high speed analog-to-digital converters. However, its design requires an accurate knowledge of the analog filter bank parameters, which is difficult to have due to the nonstationary nature of these parameters. In this paper we propose a blind estimation method for the analog filterbank parameters, which is able to cope with non-stationary input signals. This is achieved by using the notion of averaged input spectrum. The estimated parameters are used to reconstruct the samples in a least mean squares (LMS) sense. The proposed LMS design generalizes existing approaches by dropping the bandlimited assumption on the input signal. Instead, it assumes that the input has an arbitrary power spectrum which is adaptively estimated. We present numerical experiments showing the good performance of the blind estimation stage, and the clear advantage of the proposed LMS design.

Minyue Fu Programme Leader

Juan-Carlos Agüero Deputy Programme Leader


D.3 EFFICIENT SOUND SYNTHESIS FOR REAL-TIME APPLICATIONS

Researcher: Damian Marelli

The inverse fast Fourier transform (IFFT) method is a time-frequency technique which was proposed to alleviate the complexity of the additive sound synthesis method in real-time applications. However, its application is limited by its inherent trade-off between time and frequency resolutions, which is determined by the number of frequencies used for time-frequency processing.

To overcome this limitation we propose both, a time-refining and a frequency-refining technique. The combination of these two techniques permits as to achieve any time and frequency resolution, for any given number of frequencies. Using this property, we find the number of frequencies need to minimize the overall complexity. It turns out that the optimal choice is a number large enough so that frequency-refining is not needed. This leads to major complexity reduction.

D.4 ENCODING OF CONTINUOUS-TIME SIGNALS UNDER RECONSTRUCTION DELAY CONSTRAINTS

Researchers: Damian Marelli Kaushik Mahata Minyue Fu

Encoding of a continuous-time signal is commonly done by quantizing a sampled signal. When the encoded data are used to reconstruct the original continuous-time signal, this approach would need either a high sampling rate, much higher than the Nyquist rate, or a large reconstruction delay. We have proposed an alternative approach to encoding of continuous-time signals subject to the usual data rate constraint and, more importantly, a reconstruction delay constraint. We first apply a Karhunen – Loève decomposition to re-parameterize the continuous-time signal as a discrete sequence of vectors. We then build a state space model for this sequence and use a particle filtering method to dynamically encode the vector sequence. Numerical experiments show that the proposed approach is nearly optimal and offers a major advantage when compared with direct quantization of a sampled signal.

D.5 VOCAL TRACT MODEL ESTIMATION FOR SPEAKER VERIFICATION

Researcher: Damian Marelli

We investigate the use of formant and anti – formant measurements of nasal consonants for speaker verification. The features are obtained using a pole-zero vocal tract model, estimated by minimizing a logarithmic criterion. This is motivated by the perception of amplitude by the human auditory system. A GMM-UBM approach is used for performing speaker comparisons within the likelihood-ratio framework. Our results are compared with a technique based on Mel Frequency Cepstral Coefficients (MFCCs) as well as with a technique based on formant estimation. The proposed approach outperforms the formant estimation approach. It also attains performance measures comparable to those of the MFCC system, while offering a more straightforward interpretation in terms of a physical speech production model.


D.6 POWER SYSTEM STATE ESTIMATION WITH COMMUNICATION CONSTRAINTS

Researchers: Minyue Fu Xin Tai (Student) Damian Marelli

We have studied the impact of communication constraints on the performance of state estimation in power systems. These studies permit us to improve the accuracy and efficiency of state estimators with non-ideally obtained measurement data, as occur in traditional power systems. This research problem also finds application in the next-generation electric power system, i.e. the smart grid, where huge amounts of data needs to be transmitted and routed by limited communication resources, especially wireless communication networks. In our research, we have built the procedure framework of state estimation based on the IEEE 14 buses test system. Then we evaluate the estimation performance with different packet losses rates. This is done considering both I.I.D. and Markovian packet loss models. We have also studied the impact of the number of Phasor Measurement Units (PMU) on the estimation performance under communication constraints.

D.7 IMPROVED CONTROL DESIGN METHODS FOR PROXIMATE TIME OPTIMAL SERVOMECHANISMS

Researchers: Jinchuan Zheng Minyue Fu Aurelio Salton (Student) Zhiyong Chen

Minimum time optimal control for servomechanisms can generate chattering in the presence of measurement noise, feedback delays or model uncertainty. Thus it is not a practical control law. The most popular alternative approach is the, so-called, Proximate Time Optimal Servomechanism (PTOS). This approach starts with a near time optimal controller and then switches to a linear controller when the system output is close to a given target. However, the chattering problem is avoided at the expense of a slower time response. We have proposed two methods for eliminating the conservatism present in such controllers. The first method applies a dynamically damped controller that allows a, so-called, acceleration discount factor to be pushed arbitrarily close to one. The second method applies a continuous nonlinear control law that makes use of no switching. Experimental results show that the proposed designs essentially eliminate the conservatism in the traditional controller, achieving a performance comparable to the limits given by minimum time optimal control.


D.8 CONTROL DESIGN FOR DUAL-STAGE ACTUATOR SYSTEMS

Researchers: Jinchuan Zheng Minyue Fu Aurelio Salton (Student) Zhiyong Chen

We have studied tracking and disturbance rejection problems in dual-stage actuator systems under the 2-DOF control framework. It is revealed that these problems can be decoupled into two independent optimization problems, each of which can be subsequently designed to provide desired performance of disturbance rejection and step tracking. The 2-DOF controller is designed based on the doubly coprime factorization approach, with which the closed-loop transfer function is expressed explicitly in terms of design parameters. This greatly simplifies the optimization of design parameters in meeting desired specifications. We have also studied how to use the design parameters to deal with specific problems in the DSA, i.e., control allocation and trajectory planning. For step tracking beyond the secondary actuator range, a nonlinear controller is also used for the primary actuator to complete the task. Experimental work has also been carried out to demonstrate the practical implementation of the DSA control system and to verify its effectiveness for step tracking and disturbance rejection and its robust performance under load changes.

D.9 DEVELOPMENT OF DUAL-STAGE XY POSITIONING STAGE

Researchers: Minyue Fu Jinchuan Zheng

In this project, we have developed a high-speed XY positioning stage using a novel dual-stage parallel kinematics structure. The XY table is based on an inversion of the Oldham coupling. The advantages of this kinematic configuration include low inertia, uniform kinematic conditioning, and dynamically matched axes. In addition, the integration of dual-stage actuation in each axis allows high-precision motion along a large working range. The design of the XY table makes this system particularly well suited for high-speed and ultra-high precision contouring in the XY plane with a large working range. The challenge of this project also includes the control design for the multi-actuators for best cooperation to achieve the control goals. Experimental works were also conducted to evaluate the system capabilities. We have also explored many applications in several fields such as manufacturing devices and microscopy.


D.10 SEqUENTIAL BAYESIAN FILTERING VIA MINIMUM DISTORTION qUANTIZATION

Researchers: Graham Goodwin Claus Müller Mauricio Cea (Student)

External Collaborator: Arie Feuer (Technion, Israel)

Bayes Rule provides a conceptually simple, closed form, solution to the sequential Bayesian nonlinear filtering problem. The solution, in general, depends upon the evaluation of high dimensional multivariable integrals and is thus computationally intractable save in a small number of special cases. Hence some form of approximation is inevitably required. An approximation in common use is based upon the use of Monte Carlo sampling techniques. This general class of methods is referred to as Particle Filtering. In this research we have studied an alternative deterministic approach based on the use of minimum distortion quantization. Accordingly we use the term Minimum Distortion Nonlinear Filtering (MDNF) for this alternative class of algorithm. We have obtained theoretical support for MDNF and have illustrated its performance via simulation studies.

We have also developed methods which exploit recent results on an incremental form of the discrete nonlinear filter to develop a novel algorithm which is computationally straightforward at high sample rates. See G.C. Goodwin, A. Feuer and C. Müller; G.C. Goodwin and M. Cea, in Book Chapters; A. Feuer and G.C. Goodwin, in Journal Publications; M. Cea, G.C. Goodwin and A. Feuer; G.C. Goodwin, M. Cea and A. Feuer, in Conference Papers.

D.11 IDENTIFICATION OF LINEAR SYSTEMS HAVING NON-UNIFORM SAMPLING PERIOD

Researchers: Graham Goodwin Juan-Carlos Agüero Mauricio G. Cea (Student)

External Collaborators: Juan I. Yuz (UTFSM, Chile) Jared Alfaro (UTFSM, Chile)

We have considered the problem of identification of continuous time systems when the data is collected using non-uniform sampling periods. We have formulated this problem in the context of Nonlinear Filtering. We have shown how the new class of nonlinear filtering algorithm (minimum distortion filtering) described in Section D.10 can be applied to this problem. We have also compared the results with those obtained from (a particular realization) of Particle Filtering.

We have also applied the Expectation-Maximization (EM) algorithm to identify continuous-time state-space models from non-uniformly fast sampled data. We assume that the sampling intervals are small and uniformly bounded. We have modified the standard formulation of the EM Algorithm for discrete-time models, using a parametrization of the sampled-data model in incremental form. This incremental model allows us to recover the continuous-time system description as the sampling period goes to zero. See Goodwin and Cea in Book Chapters; Yuz, Alfaro, Agüero and Goodwin in Journal Publications; Yuz, Alfaro Agüero and Goodwin in Conference Publications.


D.12 ACCURACY OF LINEAR MIMO MODELS OBTAINED BY MAXIMUM LIKELIHOOD ESTIMATION

Researchers: Juan-Carlos Agüero Graham Goodwin Boris Godoy

External Collaborators: Cristian R. Rojas (KTH, Sweden) Håkan Hjalmarsson (KTH, Sweden) Alicia Esparza (UPV, Spain)

In this research we have studied the accuracy of linear Multiple-Input Multiple-Output (MIMO) models obtained by maximum likelihood estimation. We have developed a frequency domain representation for the information matrix for general linear MIMO models. We have also shown that the variance of estimated parametric models for linear MIMO systems satisfies a fundamental integral trade-off. This trade-off is expressed as a multivariable “water-bed” effect. An extension to spectral estimation has also been developed. See J.C. Agüero, C. Rojas, H. Hjalmarsson and G.C. Goodwin, in Journal Publications.

D.13 TIME AND FREqUENCY DOMAIN MAXIMUM LIKELIHOOD ESTIMATION

Researchers: Juan-Carlos Agüero Graham Goodwin Ramón Delgado (Student)

External Collaborators: Juan I. Yuz (UTFSM, Chile) Wei Tang (Northwestern Polytechnical University) Peoples Republic of China

Maximum likelihood estimation has a rich history. It has been successfully applied to many problems including dynamical system identification. Different approaches have been proposed in the time and frequency domains. In this research we have explored the relationship between these approaches and have established conditions under which the different formulations are equivalent for finite length data. A key point in this context is how initial (and final) conditions are considered and how they are introduced in the likelihood function. See J.C. Agüero, J.I. Yuz, G.C. Goodwin and R.A. Delgado, in Journal Publications; J.C. Agüero, J.I. Yuz, G.C. Goodwin and W. Tang, in Conference Publications.


D.14 ROBUSTNESS IN EXPERIMENTAL DESIGN

Researchers: Juan-Carlos Agüero James Welsh Graham C. Goodwin

External Collaborators: Cristian R. Rojas (KTH, Sweden) Arie Feuer (Technion, Israel)

We have focused on the problem of robust experiment design, i.e. how to design an input signal which gives relatively good estimation performance over a large number of systems and model structures. Specifically, we have formulated the robust experiment design problem utilizing the well known fundamental limitation on the variance of estimated parametric models as constraints. Using this formulation we have developed a closed form solution for the input spectrum which minimizes the maximum weighted integral of the variance of the frequency response estimate over all model structures. This allows the design of an input signal when only diffuse a priori information is known about the systems. See C.R. Rojas, J.C. Agüero, J.S. Welsh, G.C. Goodwin and A. Feuer, in Journal Publications.

D.15 VARIANCE OR SPECTRAL DENSITY IN SAMPLED DATA FILTERING?

Researchers: Graham Goodwin Juan-Carlos Agüero

External Collaborators: Juan I. Yuz (UTFSM, Chile) Mario E. Salgado (UTFSM, Chile)

To store, transmit and manipulate data from a continuous-time system one inevitably needs to sample. Unfortunately sampling typically involves a loss of information. It is thus important to understand the impact of sampling on continuous-time signals and systems.

In practice it is usual to mitigate the information loss resulting from sampling by introducing a-priori information. However, this raises the question of the sensitivity of the result to the veracity of the prior information.

We have shown that spectral density (rather than signal variance) plays a central role in understanding the relationship between continuous and sampled data. See G.C. Goodwin, J.I. Yuz, M.E. Salgado and J.C. Agüero, in Journal Publications; G.C. Goodwin, J.I. Yuz, J.C. Agüero and M. Cea, in Plenary Publications.


D.16 IDENTIFICATION OF SYSTEMS HAVING qUANTIZED OUTPUT DATA

Researchers: Boris Godoy Graham Goodwin Juan-Carlos Agüero Damián Marelli

External Collaborators: Torbjörn Wigren (Uppsala University, Sweden)

We have developed a novel algorithm for estimating the parameters of a linear system when the observed output signal is quantized. This question has relevance to many area including sensor networks and telecommunications. The algorithms that we have developed have closed form solutions for the SISO case. However, for the MIMO case, a set of pre-computed scenarios is used to reduce the computational complexity of EM type algorithms that are typically deployed for this kind of problem. Comparisons are made with other algorithms that have been previously described in the literature as well as with the implementation of algorithms based on the quasi-Newton method. See B.I. Godoy, G.C. Goodwin, J.C. Agüero, D. Marelli and T. Wigren, in Journal Publications; D.E. Marelli, B.I. Godoy and G.C. Goodwin, in Conference Publications.

D.17 VIRTUAL CLOSED LOOP IDENTIFICATION

Researchers: Juan-Carlos Agüero Graham Goodwin

External Collaborator: Paul M.J. Van den Hof (Delft University of Technology, The Netherlands)

Indirect methods for identification of linear plant models on the basis of closed loop data are based on the use of (reconstructed) input signals that are uncorrelated with the noise. This generally requires exact (linear) controller knowledge. On the other hand, direct identification requires exact plant and noise modeling (system in the model set) in order to achieve accurate results, although the controller can be nonlinear. In this research we have developed a generalized approach to closed-loop identification that includes both methods as special cases and which allows novel combined methods to be generated. Besides providing robustness with respect to inexact controller knowledge, the method does not rely on linearity of the controller nor on exact noise modeling. The generalization is obtained by balancing input noise decorrelation against noise whitening in a user-chosen flexible fashion. To this end, a user-chosen virtual controller is used to parameterize the plant model, thereby generalizing the dual-Youla method to cases where knowledge of the controller is inexact. Asymptotic bias and variance results have also been developed for the method. See J.C. Agüero, G.C. Goodwin and P.M.J. Van den Hof, in Journal Publications; G.C. Goodwin, in Conference Papers.


Program Goals:

The Bayesian Learning Program comprises researchers from Statistics, Engineering and Applied Mathematics backgrounds, reflecting the strong interdisciplinary nature of the Centre.

The Bayesian Learning Program has the following overall goal:

“To combine expertise in Bayesian statistics with inter-disciplinary expertise in order to solve real-world problems”

In 2010 the Program focused on the following four themes in fundamental and applied research:

n Advances in Bayesian methodologyTopics: Mixture models

n Advances in Bayesian computationTopics: sparse matrix representation

n Advances in Bayesian applicationsTopics: industry, ecology, agriculture, genetics in animal science

n Other Mathematical Applications

The Bayesian Learning Program is located primarily at qUT Brisbane, led by CI Mengersen. It has inspired the formation of the active BRAG (Bayesian Research and Applications Group) at qUT, which comprises approximately a dozen PhD students and the same number of postdocs and research associates. It has also constituted the foundation for the new qUT Centre in Data Analysis, Computation.

Major research activities in 2010 included development of theoretical, computational and applied Bayesian methods and models, publication of outputs in the form of journal articles and conference papers, conduct of national and international workshops, conduct of professional short courses, participation in national and international conferences, hosting of international visitors, visits to international laboratories, supervision of postgraduate students and collaboration with other members of CDSC. These activities are detailed below.

Program Participantsn Program Leader: Professor Kerrie Mengersen

n Academic Researchers: Dr Darfiana Nur, Professor Ian Turner

n Research Associates: Dr Clair Alston, Dr Sama Low Choy

n Continuing PhD students: Margaret Donald, Sri Astuti Thamrin

E.1 ADVANCES IN BAYESIAN METHODOLOGY

E.1.1 Mixture Models

In collaboration with Professor Rousseau (France), CI Mengersen continued to study Bayesian mixture models. In particular, they examined the common problem of analysis data with an unknown number of components, and determined that in the case of over fitting, that is, allowing the model to have more components than is the true case, that the posterior distribution is stable and will tend to empty the additional components.

BAYESIAN LEARNING (qUT NODE)

Kerrie Mengersen Program Leader

Ian Turner Deputy Program Leader

E. STATISTICS


This result is of interest in particular because it validates the use of Bayesian estimation in mixture models with too many components. It is also of interest since it is one of the few examples where the prior can actually have an impact asymptotically, even to 1st order (consistency) and where choosing a less informative prior leads to better results. It is to be noted, that the usual less informative prior are designed to favour weights close to 0, so that in the present framework they actually bring the correct information, as opposed to more informative priors which would prevent the weights from becoming small. Although still in draft format, this result is being referenced in conference proceedings and presentations, showing the interest in this area for applied statisticians.

Drs Alston, Mengersen & Kruijer collaborated on improving and validating the mixture model as an analytical tool for CT scan summarization and volume estimation. The initial research focused on modeling the known carcase components of fat, protein and ash from a trial of 50 animals, with a view to establishing a set of common boundaries that could be used to allocate pixels into a component group. A set of boundaries was established, however the error bands proved the inadequacy of such an approach to determine volume.

Boundaries were then established from the fitted mixtures, however, it became evident that the variation between animals and sites within an animal was significant. As such, the mixture has become established as the preferred model for volume calculation via CT scans. Current work is in two areas, i) Use of carcase measures to validate the mixture representation of the volume, ii) faster computational methods for the mixture model including Variational Bayes and MCMC using adaptive random scan updates for component membership, thus reducing the computational burden.

Alston & Mengersen also continued working on mixture models in the context of clustering for satellite images. The method successfully segmented chlorophyll measurements taken over the Great Barrier Reef, identifying the level of chlorophyll in inshore and offshore shelves. The second issue was monitoring levels over time, and identifying seasonal changepoints. This was achieved through adaptation of control chart techniques which originated in industrial applications.

E.2 ADVANCES IN BAYESIAN COMPUTATION

E.2.1 Sparce Matrix Representation

The problem of analyzing large datasets, particularly those with complex structures, is common in many applications such as image classification, space-time models and genetics. CIs Ian Turner and Kerrie Mengersen have extended their 2009 research which combined computational mathematics and statistics ideas to tackle this problem.

Landsat imagery was analysed by the use of Krylov subspaces which take advantage of the sparse matrix. The methodology was developed and evaluated in the context of spatial dynamic factor models with a Gaussian Markov random field used to capture the spatial dependence. The large, sparse linear system induced by this representation was shown to be much more efficiently estimated using the Krylov subspace methods. The algorithm was successful, in that it was fast and scalable, making it feasible to analyse large dimension problems. The results of this research were published in an A rank journal, JRSS C.


E.3 ADVANCES IN BAYESIAN APPLICATIONS

E.3.1 Ecology

Project 1: Elicitator SoftwareIn conjunction with the High Performance Computing Unit at qUT, CI Mengersen and qUT colleagues, notably Dr Sama Low Choy in DAMC and members of the High Performance Computing Group, have continued to develop the software tool, “Elicitator”, which is used to capture expert knowledge, in order to determine appropriate prior models in Bayesian analysis.

The current version (1.1) was extended in 2010 to achieve four outcomes:

i) Accommodate a range of data types: from probabilities (V1.0) to continuous or non-zero measurements, counts and rates;

ii) Support for geographic “sites” as well as generic “cases”;

iii) Extend statistical encoding algorithms – for all data-types*, and improve existing algorithm for encoding a Beta distribution;

iv) Update interface to R with improved error-handling.

Figure 25


Project 2: Monitoring Reef EnvironmentsThe use of satellite images to monitor the effect of natural change and human activities on landscapes is constantly increasing as the technology becomes more available and affordable. The challenge in analysing data of this nature is to determine methods of collating and summarising the information in a manner which is useful for researchers and managers, so that they may appreciate and understand the current state of the region of interest and the changes, large or subtle, that may be occurring over time.

In this research we initially used Bayesian mixture models to cluster like pixels and estimate key chlorophyll characteristics from daily images taken over the Great Barrier Reef, Australia. We then used control chart techniques to determine long term drift in chlorophyll levels, as well as sudden occurrences of extremely high levels, known as blooms. Bayesian changepoint models were used to determine when the season has changed, as seasonal transitions in tropical regions are not always well defined by the calendar year.

The Bayesian mixture model provided a succinct and informative characterisation of the satellite data, with prominent components representing the main bodies of both inshore and offshore readings. Some minor components were found to be useful in tracking potential bloom outbreaks in the inshore region. Control charts were useful for consolidating the mixture model estimates and determining if subtle or large shifts in chlorophyll levels had occurred over the trial period.

We envisage that these results could be generalised to a large range of situations where image data are taken over similar regions with the aim of detecting change in composition.

Figure 26


E.3.2 Animal Science and Genetics

Dr Clair Alston and CI Mengersen have worked on applying Bayesian methods and algorithms to problems in animal science and genetics. This has included new work with collaborators at Murdoch University (WA). They have conducted a first course in Bayesian analysis with specific reference to the linear and generalized linear mixed models which are commonly used in genetics models and growth trials in this field of research.

The Bayesian approach has proved useful for answering questions such as probability of carcase measuring > 5.7 ph given eye muscle area, and other hypothesis that are not readily answered in the standard REML framework.

Continuing collaboration is in the area of mixed models and CT scanning in conjunction with genetics data.

Figure 27


E.3.3 Agriculture

The multi-dimensional spatial models developed by PhD student Margaret Donald (with supervisors Alston and Mengersen) to an agricultural context during 2009, in collaboration with NSW Department of Agriculture, where extended to examine the change in water table levels over time.

The model through time is specifically designed to estimate the effects of the current cropping systems in the highly productive Liverpool plains area of NSW. Like many intensively farmed areas of Australia, this region is increasingly at risk of salinity, caused by excess water ‘leaking’ through to the underlying aquifers and on its way leaching out many of the soil salts. By examining the effect of the current farming methods and comparing them with alternative systems, we aim to identify a method of farming that uses the larger part of the available water, and thus allows the soil to take up new rains without this leaking phenomenon.

The data comprise a moisture value measured at predetermined access tubes using a neutron probe device. Measurements were taken at roughly monthly intervals over a 5 year period and is complicated by the phenomena of soils shrink / swell in the surface layers, prompted by wetting / drying of the soil, hence measurement error models were appropriate. On a single time analysis, the final model of choice is a complex hierarchical model, with several CAR spatial variance components and a complex fixed part described by a spline model with an errors-in-measurement component.

During 2010, two papers were submitted to A rank statistical journals.

E.4 OTHER MATHEMATICAL APPLICATIONS

E.4.1 Computational Simulation of Brain Connectivity Using Full MRI Diffusion Tensor

CI Turner and Dr Lui have been researching methods to analyse MRI scans. MRI scans are used to measure water molecule diffusion in the brain can be measured using a magnetic resonance imaging. The anisotropy of the diffusion tensor is of particular interest in brain images, as it is related to white matter fibre tracts. This research presents additional information from the brain image diffusion tensor magnetic resonance imaging (DT-MRI) of a patient with Parkinson’s disease.

This research derived numerical methods to analyse brain images and developed one-dimensional linear diffusion and fractional models to study standard diffusion and anomalous diffusion behaviour in the white matter of the brain. The brain image data was compared for a patient with Parkinson’s disease before and after surgery. We believe that the simulated information can provide the surgeon with a more fundamental understanding of the impact of surgery on the diffusion behaviour in the white matter of the brain.


STATISTICAL INFERENCE AND MODELLING – (UON NODE)

Program Goals:

This program investigates advances in statistical thinking through the application and development of cutting edge analytic tools necessary for understanding data and its properties. This is achieved through research into many aspects of statistical theory and application that are designed to help solve specific problems and is conducted in collaboration with national and international experts in a diversity of areas. The benefit of undertaking this area of research ensures that researchers are provided with the appropriate statistical techniques needed for the efficient and effective interpretation of their data. It also provides important advances of knowledge in all branches of learning and enterprise.

The program’s mission is to provide appropriate and innovative contributions to the development of statistical theory, modelling, analysis and reporting with a primary focus on application. The breadth and depth of the applications and theoretical investigations are undertaken by a group with established successful collaborative links with researchers in the professional and community sectors and who espouse knowledge, accessibility, professionalism and friendliness.

The group achieves these goals through the members’ established research interests and outcomes, continued individual innovation and self-development, collaborative efforts with both intra – and inter-institutional researchers and the community, involvement with community and professional societies, continued journal publications, grant applications and peer-reviewing of journals’ submitted articles.

In 2009, the group attracted two new academic staff members with an established national and international research profile: Eric Beh and Frank Tuyl. Between them, they add an extra dimension to the enthusiasm and diversity of statistical thinking that has developed within the group over the years. In December, the statistics group hosted the Third Annual ASEARC Conference that saw delegates from a number of Australian universities attend and attracted representation from international researchers from Italy, Belgium, South Africa and New Zealand.

Professor John Rayner UoN Node Program Leader

Associate Professor Eric Beh UoN Deputy Program Leader


E.5 CATEGORICAL DATA ANALYSIS

Researchers: Eric Beh John Rayner

External Collaborators: Rosaria Lombardo (Second University of Naples, Italy), Luigi D’Ambra (Second University of Naples, Italy), Biagio Simonetti (University of Sannio, Italy), Thomas B. Farver (University of California, Davis, USA) Derek Smith (Director, Workcover Research Centre of Excellence, University of Newcastle)

This project deals with many issues concerned with the analysis of categorical data. Such data commonly arises from many types of surveys and questionnaires designed to elicit information from scientific or social research.

E.5.1 Graphical Analysis of Categorical Data

Much of focus of this project aims at advancing the mathematical and practical issues surrounding correspondence analysis. Such a procedure is used for graphically identifying the association structure between multiple categorical variables and allows a researcher to visually inspect how their data behaves in a low-dimensional space. The study of correspondence analysis dominates much of the European perspective of categorical data analysis, and is rarely considered in any depth by Australian researchers. Therefore this aspect of the project provides a unique glimpse into better understanding how categorical variables are associated and draws on the experience of Italian statisticians who have devoted their academic careers researching issues concerned with correspondence analysis.

E.5.2 Numerical Analysis of Categorical Data

To complement the graphical summaries of association, numerical measures are equally important and form part of the mathematical structure of these visualisation techniques. There are many different ways association can be quantified and this part of this project investigates ways in which many complex association structures can arise. This has been an ongoing research interest of many linked with the project and the collaboration with Italian researchers has been instrumental in its success. The 2009 activities have also seen two papers, jointly published by the UoN project members, appearing in international journals and are both concerned with the popular correlation measure. They have demonstrated that is can be generalised to take into consideration previous notions of categorical association rarely investigated. Other mathematical aspects concerning categorical data analysis include parameter estimation issues for log-linear models, another important focus of the project, and one that has benefited from research collaborations with the US.


E.6 ANALYSING AND REPORTING CLINICAL INDICATORS USING BAYESIAN HIERARCHICAL MODELS

Researchers: Peter Howley Frank Tuyl

External Collaborators: Robert Gibberd (School of Medicine and Public Health, UoN) Stephen Hancock (School of Medicine and Public Health, UoN) Sheuwen Chuang (Taipei Medical University)

Dr Howley’s research focuses on Bayesian hierarchical modelling and its application to foster quality improvement activity in health care, through the creation of improved methods for analysis and reporting of clinical indicator data. This relates well with his research into ‘performance measures’, which extends beyond the health care field.

His ongoing research in this area explores a) the potential for a new control chart based on Bayesian models to improve the monitoring of a healthcare organisation’s clinical indicators; b) new methods for sampling approaches to be employed as part of the Australian Council on Healthcare Standard’s CI programme; and c) a systems theory based approach to the improvement and quality of healthcare. Dr Howley and his international collaborator, Dr Sheuwen Chuang, Taipei Medical Univeristy, won an ASSA-ISL Bilateral Program Grant “Using clinical indicators to facilitate quality improvement via the accreditation process: the control relationship” researching the extent to which accreditation surveyors and healthcare organisations utilise the existing quality measurement and reporting (qMR) systems, the perceived utility of the qMR system and the potential to better align the accreditation and qMR systems for better directing policy and the improvement of health care. This study is one in a series of adaptive-control studies developed to explore relationships between segments within the systems relationship model. It will provide a platform for ensuing studies, and development, of the systems relationship model and feedback architecture required to improve the quality of health care.

Figure 28: New posterior predictive-based control chart for monitoring indicators.


In November 2010, Dr Howley presented his collaborative work on reporting, analysing and systems theory-based improvement of the clinical indicator and accreditation system of the Australian Council for Healthcare Standards at Taipei Medical University, Taiwan and as an invited speaker at the Department and Institute of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan.

Following his presentations, Dr Howley was invited to personal meetings with the Executive Officer of Taiwan’s Joint Commission on Hospital Accreditation and a Professor of the College of Medicine and Public Health, having already been invited to personal meetings with the GM (Medical Affairs Division) of The Bureau of National Health Insurance and President of the Taiwan College of Healthcare Executives.

Figure 29: Peter Howley addresses the Taipei Medical University, Taiwan, audience

E.7 CONSENSUS PRIORS FOR BAYESIAN ANALYSIS

Researchers: Frank Tuyl Peter Howley Kerrie Mengersen (qUT)

External Collaborator: Richard Gerlach (University of Sydney)

An advantage of considering the Bayesian approach to statistical thinking is that it allows for the possible inclusion of prior information/knowledge. However, it is also desirable, for scientific communication and sensitivity analysis, to always base inference on “noninformative” or “consensus” priors. The currently recommended methodology to obtain such priors is reference analysis, but our research indicates that this methodology is incomplete. When dealing with extreme data, reference priors can be shown to be too informative. This is especially the case, for example, when considering the binomial and Poisson parameters.

One investigation concerns the identification of alternative criteria when reference analysis fails. Studying appropriate noninformative priors in applications related to this aspect of Bayesian analysis, such as logistic and Poisson regression, is an important aspect of this project.

Another important aspect of this project concerns the development of consensus intervals, given consensus posteriors for various popular models. The usual Bayesian, or credible, intervals are central, based on equal tails, and highest posterior density (HPD). The former tend to suffer from poor minimum frequentist coverage, the latter from lack of invariance under transformation. We have demonstrated that intervals based on the metric for which the consensus prior is uniform, i.e. “UHPD” intervals (where UHPD stand for uniform prior based HPD), appear to address both these issues. For models with nuisance parameters, typically several different likelihood functions of the parameter of interest exist (e.g. profile, modified profile, conditional, marginal, integrated). Current research suggests that the “right” likelihood for interval calculation does not automatically follow for any given model. But once a preferred likelihood is chosen, the UHPD metric follows from the ratio of the consensus posterior and this likelihood; note that this metric may be different from the one corresponding to the metric that follows from the consensus prior.

E.8 BAYESIAN HIDDEN MARKOV MODEL IN DNA SEqUENCE SEGMENTATION MODELLING

Researchers: Darfiana Nur Kerrie Mengersen (qUT)

External Collaborators: Yan-Xia Lin (University of Wollongong) Judith Rousseau (University of Paris-Dauphine) Ross McVinish (University of queensland)

Driven by vast amounts of DNA sequence data are currently available for analysis such as the Human Genome Project, there is an increasing need to develop efficient computational and statistical tools to analyse this biological data. In particular, many genome sequences display heterogeneity in base composition in the form of segments of similar structure.

Many statistical techniques have been developed to identify these homogeneous DNA segments, the most popular one is Hidden Markov model (HMM), which is in essence, is a mixture model with Markov dependent component indicators. The use of HMMs for segmentation modelling is now one of the old statistical methods in the young science of bioinformatics. Bayesian analysis was introduced for biological sequence analysis in early 1990s. Since then the use of Bayesian inference procedures and algorithms has revolutionized the field of computational biology. A major controversial aspect


of the Bayesian method is the need for prior distributions for the unknown parameters. Since the choice of priors injects subjective judgements into an analysis, Bayesian methods have been regarded as less objective than their frequentist counterparts. However, these subjective elements, if made explicit and treated with care, should not undermine the scientific results of the investigation. Therefore to validate the conclusions of a Bayesian analysis, a sensitivity analysis, an analysis of how the inferential statements vary for a reasonable range of prior distributions, is usually performed. The overall aim of this project is to implement the use of sensitivity analysis on priors in Bayesian analysis of DNA sequence segmentation using Hidden Markov models.

E.9 HIERARCHICAL APPROACHES TO META-ANALYSIS

Researchers: Elizabeth Stojanovski Darfiana Nur Junaidi Junaidi (Student)

External Collaborators: Kerrie Mengersen (qUT) Kanya Honoki (Nara Medical University)

Meta-analysis is the combining of results from several related studies. Information regarding associations from separate studies can sometimes be limited and/or conflicting. This may be partly attributed to differences between studies which can be considered sources of statistical heterogeneity. Random-effects Bayesian meta-analysis models are considered to combine reported estimates from several studies by allowing major sources of variation to be taken into account: study level characteristics and both between and within study variance. Observed risk ratios can be assumed random samples from study-specific true ratios, which are themselves assumed distributed around an overall ratio. These are compared against models which allow hierarchical levels between the study-specific parameters and the overall distribution. The latter model can thus accommodate partial exchangeability between studies, acknowledging that some studies are more similar due to common designs, locations and so on.

Work in progress includes combining studies that assess associations between p16INK4a status and two-year survival to incorporate between study characteristics. By allowing for differences in study design this strengthens findings of results that do not consider these study characteristics.

E.10 SMOOTH TESTS OF GOODNESS OF FIT

Researchers: John Rayner Eric Beh John Best (Conjoint) Paul Rippon (Student)

External Collaborator: Olivier Thas (Ghent University, Belgium)

The one sample goodness of fit problem is to assess whether a data set is consistent with a specified probability distribution or model. In the smooth approach, the probability density function of the specified distribution is nested in a rich family of alternatives constructed using orthonormal functions. Typically the test statistic is the sum of squares of components that are asymptotically independent and asymptotically standard normal. The components are readily interpreted and yield powerful focused tests, while the sum of squares of components yields powerful omnibus tests.

During 2010 Professor Rayner visited Dr Thas in Belgium twice. The first visit, in May, progressed several joint projects. The second, in October, was to serve on the PhD jury of Mr B. De Boeck, who successfully completed a PhD under Dr Thas’ supervision on Informative Smooth Tests of Goodness of Fit.

Figure 30: Histogram of data hypothesised to be normal, a fitted normal density (full line) and two improved densities. The dashed density is less complex than the dotted density but does not agree as well with the data.


Recent work has extended the applicability of the smooth approach to testing goodness of fit for distributions outside of the exponential family. These techniques can test for very general distributions, provided that enough moments exist for the orthonormal polynomials to be constructed. Several papers have been published that demonstrate the implementation of these methods for particular distributions. For example, articles on the beta-binomial, Rayleigh and gamma distributions and discrete distributions with ecological applications were either accepted for publication or published this year.

Work in progress includes testing for the generalised Pareto distribution. This distribution is important for modelling extreme events such as maximal flood levels. We chose to work on it because it challenged the current state of our tools, since this distribution has only finitely many moments, and this means a limited number of orthonormal polynomials are available. We have also extended to bivariate distributions work published in 2008 on construction of orthonormal polynomials. This will allow us to construct smooth goodness of fit tests for bivariate distributions including copulas, an area of much current interest.

Other work in progress includes testing for generalised linear models. These models are very pervasive in statistics, and include, for example, the analysis of variance and multiple linear regression models.

In related work, J.C.W. Rayner and E.J. Beh “Towards a better understanding of correlation”, Statistica Neerlandica, Vol.63, pp.324-333, is one of seven papers in the listed Highlights at the Statistica Neerlandica home page for 2009/2010.

E.11 qUANTILE DISTRIBUTIONS

Researchers: Robert King Ben Dean (Student)

External Collaborators: Norou Diawara (Old Dominion University, USA) Paul van Staden (University of Pretoria, South Africa)

Flexibly shaped distributions, defined via their quantile functions, provide a parametric approach for modelling data with skewness or tailweight that differs from standard distributions. This project concentrates on parameter estimation and modelling using such distributions.

Such modelling includes a variety of regression methods, with normal distributions replaced by the generalised lambda distribution.

The project also includes work to extend these quantile distributions into multivariate forms.

Other work in progress includes the use of L-Moments for estimation, new parameterisations and new distributions.


F. DISTRIBUTED SENSINg AND CoNTRoL

Program Goals:

In this program we aim to develop new areas of research, which build on our work in the other Programs, in the area of distributed sensing and control. Our goal is to establish and foster collaborative research arrangements with other Schools within the University of Newcastle as well as with internationally renowned external researchers and industry in both fundamental and applied research. We are currently working in four main areas, Adaptive Optics, Biomedical Science, Power Transformer Modeling and Robotics.

F.1 SYNAPTIC PLASTICITY-BASED DYNAMIC MODEL FOR EPILEPTIC SEIZURES

Researchers: James Welsh Graham Goodwin

External Collaborator: Mazen Alamir (CNRS, Grenoble, France)

We have developed a new dynamic model describing the epileptic seizures initiation through transition from interictal to ictal state in a brain predisposed to epilepsy is suggested. The model follows Freeman’s approach where the brain is viewed as

a network of interconnected oscillators. The proposed nonlinear model is experimentally motivated and relies on changes in synaptic strength in response to excitatory spikes.

This model exhibits a threshold beyond which a bifurcation towards a short-term plasticity state occurs leading to seizure onset. A resulting explanatory assumption is that when considering epilepsy, abnormally low thresholds towards short-term synaptic plasticity characterize brain regions. It is shown by simulation that the proposed model enables some experimentally observed qualitative features to be reproduced. Moreover, a preliminary discussion on the impact of the underlying assumptions on the fundamental issue of seizure control is proposed through an EEG-based feedback control scheme.

James Welsh Programme Leader

Rick Middleton Deputy Programme Leader

Figure 31


F.2 POWER TRANSFORMER MODELING

Researchers: Steven Mitchell James S. Welsh

A power transformer will yield a frequency response that is unique to its mechanical geometry and electrical properties. Changes in the frequency response of a transformer can be potential indicators of winding deformation or other structural problems. A commonly used diagnostic tool to detect such changes is Frequency Response Analysis (FRA). To date, FRA has been used to identify changes in a transformer’s frequency response but with limited insight into the underlying cause of the change. However there is now a growing research interest in specifically identifying the structural change in a transformer directly from its FRA signature.

This year we have investigated methods of supporting FRA interpretation through the development of transformer models, based on three different FRA tests, in order to identify the physical structural change in a transformer. The resulting models have been used as a flexible test bed for parameter sensitivity analysis, leading to greater insight into the effects that geometric change can have on a transformer FRA. To date our research has demonstrated the applicability of this modeling approach by simultaneously fitting each model to the corresponding FRA data sets and quantitatively assessing the accuracy of key model parameters. This work is a fundamental step in our efforts to develop a system that can detect the level of winding deformation in a power transformer.

Figure 32


F.3 ESTIMATION OF PARAMETERS FOR A SYNCHRONOUS MACHINE AT LIDDELL POWER STATION

Researchers: James Welsh Boris Godoy

This project investigates methods for the estimation of parameters of large synchronous machines, which are of great importance in power generation. In particular one of the large synchronous generators at Liddell Power station is studied in this phase of the project.

The work to date has focused on parameter estimation using the standstill frequency response (SSFR). To estimate the parameters of the machine, a range of input signals, with test frequencies from 0.5 mHz to 1 kHz, are applied. Using the recorded response to these inputs, it is then possible to estimate the parameters associated with equivalent circuits in the d-q axis Park transformation using a method of indirect inference.

Obtaining the parameters for the equivalent circuit models is an intermediate step in this project. In fact, the final aim is to identify the parameters when the machine is under working conditions, that is, connected to the power grid. Hence, modeling of the non-linear characteristics such as saturation is necessary, as well as the modeling of the exciter.

One of the model responses, for a synchronous generator at Liddell Power Station, is compared with the collected data as shown below.

F.4 ROBOTICS RESEARCH

Researchers: S. Bhatia (Student) Stephen Nicklin (Student) Jason Kulk (Student) Aaron Wong (Student) Stephan Chalup Robert King James Welsh Rick Middleton

The NUbots from the University of Newcastle have had a strong record of success in RoboCup since first entering in 2002. The Nubots achieved 3rd place in 2002 (Fukuoka), and again in 2003 (Padua) and 2004 (Lisbon). In 2005 (Osaka) with a complete redevelopment of the code, the NUbots came 2nd in a heartbreaking penalty shoot out against the German Team. With a strong 2005 base code, 2006 development focused on optimising the existing system. The result was a nail biting final against rUNSWift in Bremen where the NUbots won 7-3. In 2007 we were once again runners up, losing the final in Atlanta against the Northern Bites.

2008 involved the transition from the Sony ERS-7 robot to the Aldebaran Nao. We created a joint team, The NUManoids with the the National University of Ireland, Maynooth (NUIM). Together we successfully overcame the challenges of adapting to the new hardware to become the first world champions of the Robocup Nao SPL. In 2009 we returned as a standalone team entering once again as the NUbots, completing the competition as quarter finalist.

RoboCup in 2010 was held in Singapore. This gave us an opportunity to visit the PSB campus to demonstrate and discuss our work. In 2010 the NUbots system was redesigned from the ground up with the goal of allowing it to be run on multiple platforms. The NAO, Webots simulator, Cycloid and the Bear Robots all run using the same NUbots system. 2010 was a very competitive year at RoboCup, and we were knocked out of the final pool stage.

F.4.1 Multimodal UKF Localization Using Visual Landmarks and Odometery Motion Models

Researchers: Stephen Nicklin (Student) S. Bhatia (Student) James Welsh Robert King

The effectiveness of a localisation algorithm depends on the quality of observations made, amount of processing power available, availability of unique landmarks, and knowledge of initial position. Such inputs are limited in the RoboCup Standard Platform League (SPL). This research focuses on making the best possible use of available data. The general method used involves a multiple hypothesis unscented Kalman filter. The different hypotheses are used to compare decisions when faced

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with ambiguous measurements. One of the major challenges in this are involves minimizing the number of hypothesis required while still making correct decisions. Other areas involve cooperative localisation using multiple agents.

Another area of interest related is active localisation, this is where the robot makes behavioral decisions in order to improve its localization. The objects that the robot views greatly affect its estimated position. Some objects give more information than others, and require different amounts of effort to view. This leads to a complex optimisation problem with many variables and much uncertainty involved.

F.4.2 A Study of Manifold Alignment Using an Artificial Acoustic Dataset for Robot Localisation

Researchers: Aaron Wong (Student) Stephan Chalup

Localisation is an important aspect of any autonomous robotic agent, where many systems use visual cues to localise. However, visual information is not always available. Additional acoustic cues could be used to assist localisation. In this short paper we study the application of aligning acoustic cues to assist localisation. We examine the use of manifold alignments without correspondance to an artificially generated acoustic sound-scape dataset. We describe a modular manifold alignment system, in which we explore the effects of different variables in the alignment and the dimensionality reduction process.

F.4.3 Evaluation of Walk Optimisation Techniques for the NAO Robot

Researchers: Jason Kulk (Student) James Welsh

Locomotion performance is a critical component of any successful robotic soccer team. The procedure of optimising a walk engine has a high cost in both resources and time.

In this paper we evaluate the performance of three different optimisation algorithms, Evolutionary Hill Climbing with Line Search (EHCLS), Policy Gradient Reinforcement Learning (PGRL), and Particle Swarm Optimisation (PSO); two different fitness functions, one speed--based and another efficiency--based; and two different parameter spaces, one expanded to include joint stiffnesses. For each configuration the average performance, variance, fall percentage and quality of output parameters is assessed in a simulator.

The EHCLS and PSO have similar performance, however both result in a large number of falls. The PGRL has slightly lower performance, but causes the robot to fall less frequently.

Both fitness functions produce walks with similar speeds, however the efficiency--based measure reduces the number of falls during an optimisation episode, and produces walks that are more stable. The expansion of the parameter space to include the stiffness also improves the stability of the tuned parameter--sets.

F.4.4 Perturbation Sensing for Humanoid Robots Using a Multiclass Support Vector Machine

Researchers: Jason Kulk (Student) James Welsh

Research on human posture control reveals that proprioception is a crucial sense for the control of stance. In particular, proprioceptive cues in the ankle, hips, and trunk trigger automatic postural responses to perturbations. Generally, the robotics literature discusses the use of the Zero Moment Point to stabilise stance, where as, humans can not calculate this point from proprioceptive cues, instead joint velocity information is used.

This paper applies the use of proprioception to a humanoid robot, where the joint velocities are used directly to detect and determine the direction of a perturbation during quiet stance. A weighted Manhattan norm of the joint velocity vector is used for perturbation detection and a Support Vector Machine is used to classify a perturbation as belonging to a set of directions and locations.

On application to a 23 degree of freedom humanoid the average detection time is 120ms. The direction is classified with 100% accuracy and the location on the body of the perturbation is classified with 99.92% accuracy. The majority of proprioceptive information was provided by the upper body, in particular, from the shoulders.


G. MAThEMATICAL SySTEMS ThEoRy

Program Goals:

Program Goals: The object of this Program is to expand a growing battery of mathematical knowledge and techniques that allow us to better understand both continuous and discrete dynamic systems exhibiting complex behaviour. As such it involves advances in both fundamental mathematics and their translation into applications. In addition, the program includes Newcastle researchers who are internationally recognised as leading experts in optimization and linear and nonlinear analysis. By interacting with other programs, this strength is exploited to assist help solve problems arising in various projects being undertaken across the Centre.

Research in 2010 focused on the following.

G.1 BEYOND THE SPECTRUM

Researchers: George Willis F. Ghahramani (Winnipeg) R. J. Loy (ANU) C. Read (Leeds) V. Runde (Edmonton)

The Fourier and Laplace transforms convert convolution multiplication in an algebra to pointwise multiplication of functions. Similarly, diagonalization of matrices reduces the matrix product into multiplication of eigenvalues. These techniques for simplifying multiplication rely on embedding the multiplication into an ambient commutative Banach algebra. Associated with this algebra is a space, sometimes called the maximal ideal space or the spectrum of the algebra, on which the transformed functions are supported. However, this space need not be non-empty in general (for example, nilpotent matrices and Volterra integral operators cannot be represented as functions on a space) and in this situation the algebra is called radical. This project aims to develop general techniques that may be used to analyse radical algebras, thus replacing spectral methods when they yield no information.

G.2 NONLINEAR ANALYSIS, OPTIMIZATION AND FIXED-POINT THEORY

Researchers: Jonathan Borwein Brailey Sims Miroslav Bacak (Post Doctoral Fellow, Newcastle) Sompong Dhompongsa (Thailand) Kazimierz Goebel (Poland) Henryk Hudzyk (Poland) Maria A. Japon Pineda (Spain) Art Kirk (USA) Chris Lennard (USA) Gang Li (China) Enrique Llorens Fuster (Spain) Francisco Eduardo Castillo Santos (Student supported by CONACYT scholarship from the Mexican Government) Ian Searston (Student) Matt Skerritt (Student)

Brailey Sims Programme Leader

Jose De Doná Deputy Programme Leader

George Willis Research Coordinator


The study of many situations in the behavioural, communication, computational, economic, life and physical sciences and in engineering and technology via mathematical models leads to fundamental problems in linear and more frequently nonlinear analysis. For instance; equilibria of discrete and continuous dynamical systems correspond to fixed points of nonlinear maps on infinite dimensional function spaces. The convergence and ergodic structure of orbits and various iterative schemes, such as those of Ishikawa, relate to the stability and long-term average behaviour of the system.

The solution of nonlinear optimization and control problems involves convex, smooth and non-smooth analysis and lead to variational inequalities and thence to the search for fixed points of related nonlinear operators.

Some of our principal focuses are listed below.

Analysis in the absence of linearityIn many situations there is no natural linear structure present, instances include:

n State spaces, where it often makes sense to measure how near one state is to other, but adding or scaling states may make no sense. The spacial states in which a Robot may find itself provides an example (see work by Robert Grist and his group at Uni. of Illinois).

n Cognitive models of recognition in which the genus of an object is identified with that of its nearest prototype; Voronoi cells/tessellations, including aspects of pattern recognition and image reconstruction.

n Fixed point theory on non-convex domains; for example, star-like domains.

Recently it has been observed that certain geodesic (or Menger convex) metric spaces, in particular the so called CAT(0) spaces, provide a very general setting in which a rich analysis seems possible.

A group of us (Bacak, Borwein, Searston and Sims) are working to extend many aspects of the linear analysis; fixed point theory and iterative algorithms, into this more general setting. A key tool is the analogue of a weak topology for CAT(0) spaces.

Metric fixed point theoryNonexpansive maps arise when modelling conservative or dissipative situations and their fixed point theory presents a tantalizing intermediary between the classical theorems of Banach and Brouwer which has led to a fertile interplay with metric geometry. A principal goal is to further our understanding of nonexpansive and related types of mappings, with an emphasis on identifying widely applicable, easily verifiable conditions on Banach, and more generally metric, spaces that ensure the existence of fixed points for all nonexpansive self-mappings of appropriate nonempty domains; closed bounded, convex and weakly compact sets. We are also interested in exploring effective algorithms by which fixed points can be approximated. Special emphasis is given to the more difficult cases, where the underlying space lacks the nice geometric structure of, for example, a Hilbert space, or where convexity, or linear structure, is absent.

Projection algorithmsFirst considered by von Neumann in 1932, alternating projection algorithms and variants of them have provided effective iterative procedures for solving the feasibility problem; find a point in the intersection of a family of constraint sets, and consequently have become standard tools for solving inverse and signal/image reconstruction problems.

When all of the constraint sets are convex subsets of a Banach space these algorithms have a rigorous theoretical under-pinning. However, despite the absence of a sound theoretical justification, for more than three decades these same


algorithms have been routinely employed to successfully solve real world problems involving non-convex constraints. Our investigations seek to provide theoretical foundations for these applications.

Two approaches are being explored: the application of techniques from the theory of difference equations and the extension into CAT(0) spaces, where convexity is relaxed and replaced by the broader notion of metric convexity.

The extension to best proximity points when the feasible set is empty is also under investigation.

This research promises to improve the performance and quality of many practical signal reconstruction methods used by varied (Australian) industries from telecommunication to mining and by researchers in the digital arts and fields such as astronomy, physics, chemistry, bioscience, geoscience, engineering and health.

Ultraproduct MethodsBanach space ultra-products, and more recently ultra-products of metric spaces, represent a common meeting ground between standard and non-standard analysis, and have become powerful tools in both linear and nonlinear analysis. By lifting the problem to an ultra-power approximate solution become exact solutions (for example; an approximate eigenvalue of an operator corresponds to an eigenvalue of the lifted operator). Refinements of these techniques and their application to a wide variety of problems remain major focuses.

G.3 qUATERNIONIC SIGNAL PROCESSING

Researchers: Jeff Hogan Andrew Morris (Student)

This project is aimed towards the construction of analysis and processing tools for multi-channel signals, such as colour images, using a multi-dimensional version of complex analysis known as Clifford analysis. Achievements this year include:

n Determination of conditions on quaternionic filters that are necessary if they are to generate orthonormal or biorthogonal wavelets.

n An exploration of transformations that preserve quaternionic orthogonality and biorthogonality

n Construction of non-standard examples of translation-invariant quaternionic-linear operators and associated translation-invariant closed subspaces.

n Construction of non-separable quaternionic biorthogonal wavelet bases.

G.4 SAMPLING IN PALEY-WIENER SPACES

Researchers: Jeff Hogan Joseph Lakey (New Mexico State University, USA)

Work has continued on aspects of sampling theory and numerical analysis related to the prolate spheroidal wavefunctions. In particular, we have developed bounds on the norm of the quotient and remainder in an analogue of the division algorithm for bandlimited signals. This has led to a demonstration of the effectiveness of an approximate quadrature scheme for bandlimited signals in which the nodes are zeroes of an appropriate prolate.

pUBLICATIoNS 2010



BOOKS

J.M. Borwein and P.B. Borwein“Selected Writings on Experimental and Computational Mathematics”, Psi Press, October 2010.

BOOKS IN PREPARATION

S.O.R. Moheimani and E. Eleftheriou* (Eds.).“Dynamics and Control of Micro and Nanoscale Systems”, Springer-Verlag, Germany, to appear 2011.

T. Perez and T.I. Fossen*“Hydrodynamics of Marine Craft for Simulation and Motion Control”.

J. Yuz* and G.C. Goodwin“Sampling in Digital Signal Processing and Control”

CHAPTERS IN BOOKS

J.M. Borwein and D.R.Luke“Entropic Regularization of the [image: l0] Function”, Chapter 5 in “Fixed-Point Algorithms for Inverse Problems in Science and Engineering”, Springer Optimization and Its Applications, pp.65-91.

J.M. Borwein and B.Sims“The Douglas-Rachford algorithm in the absence of convexity”, Chapter 6,” Fixed-Point Algorithms for Inverse Problems in Science and Engineering”, Springer Optimization and Its Applications, pp.93-109.

G.C. Goodwin, A. Feuer* and C. Müeller“Sequential Bayesian Filtering via Minimum Distortion quantization”, Chapter in “Three Decades of Progress in Control Sciences”, Hu, X.; Jonsson, U.; Wahlberg, B.; Ghosh, B. (Eds.), ISBN: 978-3-642-11277-5, Springer, October, 2010.

G.C. Goodwin, J.I. Yuz* and J.C. Agüero“Models for Sampled-Data Systems”, Chapter in “The Control Handbook (2nd Ed)”, W.S. Levine (Ed), ISBN: 9781420073669, CRC Press, December, 2010.

D.P. Looze*, J. S.Freudenberg*, J. H. Braslavsky and R. H. Middleton*“Tradeoffs and limitations in feedback systems”, The Control Handbook, Second Edition, W.S. Levine (Ed). CRC Press, Taylor and Francis, December 2010.

S. Low Choy, J. Murray, A. James and K. Mengersen“Indirect Elicitation from Ecological Experts: From Methods and Software to Habitat Modelling and Rock-wallabies”, Chapter in “The Oxford Handbook of Applied Bayesian Analysis”, eds A. O’Hagan and M. West (Eds), pp.511-544.

T. Perez and T.I. Fossen*“Motion Control of Marine Craft”, Chapter 32 in “The Control Handbook, 2nd Edition”, Vol. Control Systems Applications. W.S. Levine (Ed). CRC Press, Taylor and Francis, December 2010.

M.E. Salgado* and G.C. Goodwin“Architectural Issues in Control System Design”, Chapter in, The Control Handbook (2nd Ed), W.S. Levine (Ed), ISBN: 9781420073669, CRC Press, December, 2010.

E.I. Silva*, J.C. Agüero, G.C. Goodwin, K. Lau and M. Wang“The SNR Approach to Networked Control”, Chapter in, The Control Handbook (2nd Ed), W.S. Levine (Ed), ISBN: 9781420073669, CRC Press, December, 2010.

BOOK CHAPTERS IN PREPARATION/TO APPEAR

G.C. Goodwin and M. Cea“Application of Minimum Distribution Filtering to Identification of Linear Systems using Nonuniform Sampling Period”, Chapter in Book Dedicated to Peter Young, Springer Verlag, 2011.

T. Perez and T.I. Fossen*“Hydrodynamic Models for Motion Control Design and Testing”, in preparation for “Aide-memoire of practical ship hydrodynamics”.

T.I. Fossen* and T. Perez“Motion Control Systems for Ships”, in preparation to appear in “Aide-memoire of practical ship hydrodynamics”.

PLENARY AND KEYNOTE ADDRESSES

J. Borwein“Exploratory Experimentation and Computation”, Plenary Address, 2010 German Mathematical Society Meetings (joint with Mathematical Education), Munich, 8 March 2010.

J. Borwein“The Arithmetic of 3 and 4 Step Random Walks”, Keynote Address, AMSI-CARMA Workshop on Exploratory Experimentation and Computation Theory, CARMA, July 2010.

J. Borwein“Maximum Entropy and Projection Methods for Inverse Problems”, Plenary Address, Second South Pacific Conference on Mathematics, New Caledonia, Nouméa, August 2010.

J. Borwein“Short Walks and Ramble Integrals: The Arithmetic of Uniform Random Walks”, Plenary Lecture, 54th Australian Mathematical Society Annual Meeting, The University of queensland, Brisbane, 27-30 September 2010.

J. Borwein“Life of Pi,’ Public Lecture, 54th Australian Mathematical Society Annual Meeting, The University of queensland, Brisbane, September 2010.

J. Borwein“Douglas-Ratchford Iterations in the Absence of Convexity”, Keynote Address, AMSI-CARMA Workshop on Applied Functional Analysis, CARMA, October 2010.

M. Fu“Networked Control Systems: Opportunities and Challenges”, Plenary Address, 21st Chinese Process Control Conference, Hangzhou, August 2010.

* Denotes international author.


M. Fu“Network Based Control and Estimation Problems”, Plenary Address, 11th International Conference on Control, Automation, Robotics and Vision, ICARCV, Singapore, December, 2010.

G.C. Goodwin“Sampling and Data Compression in Signal Processing and Control”, Plenary Address, International Conference on Instrumentation, Control and Information Technology (SICE2010), Taipei, Taiwan, 18-21 August, 2010.

G.C. Goodwin“Architectural Issues in Control Systems Design”, Plenary Address, Nordic Process Control Workshop (NPCW2010), Lund, Sweden, 25-27 August, 2010.

G.C. Goodwin, D.Q. Mayne*, T. Chen, C. Coates, G. Mirzaeva and D.E. Quevedo“Opportunities and Challenges in the Application of Advanced Control to Power Electronics and Drives”, Plenary Address, IEEE International Conference on Industrial Technology (ICIT2010), Valparaiso, Chile, March, 2010.

G.C. Goodwin and M.M. Seron“Some Applications of Optimization in Control Engineering”, Plenary Address, International Conference on Optimization: Techniques and Applications (ICOTA8), Shanghai, China, 10-13 December, 2010.

G.C. Goodwin, J.I. Yuz*, J.C. Agüero and M. Cea“Sampling and Sampled-Data Models”, Plenary Address, American Control Conference (ACC2010), Baltimore, USA, 30 June-2 July, 2010.

K. Mengersen“Where are they and what do they look like? Discovering patterns in data using statistical mixture models”, Public Lecture, Mixture estimation and applications, Edinburgh, 3-5 March 2010.

T. Perez“Ship Roll Motion Control”, Plenary Address, 8th IFAC Conference on Control Applications in Marine Systems, Rostock, Germany, 15-17 September.

B. Sims“The Douglas-Ratchford Algorithm in the Absence of Convexity”, Keynote lecture, First Workshop in Fixed Point Theory and Applications, Iran, June 2010.

B. Sims“Nonlinear Analysis in CAT(0-Spaces”, Keynote lecture, 2nd Asian Conference on Nonlinear Analysis and Optimization, Thailand, September 2010.

PATENTS

A.J. Fleming“A Positioning System and Method”, Published Application PCT: WO 2010/040185 2010

G.C. Goodwin, M. Cea and T. Wigren*“quantized Zooming in Inner Loop Power Control”, Patent #31996.

JOURNAL PAPERS

J.C. Agüero, J.I. Yuz* and G.C. Goodwin“Discussion on: “Identification of ARX and ARARX models in the presence of input and output noises”, European Journal of Control, Vol.16, No.3, pp.256–257, 2010.

J.C. Agüero, J I. Yuz*, G.C. Goodwin and R. Delgado“On the equivalence of time and frequency domain maximum likelihood estimation”, Automatica, Vol.46, No.2, pp.260–270, 2010.

C.L. Alston and K.L. Mengersen“Allowing for the effect of data binning in a Bayesian normal mixture model”, Comptutational Statistics and Data Analysis, Vol.54, No.4, pp.916-23, 2010

M. Bacák and J.M. Borwein“On difference convexity of locally Lipschitz functions”, Optimization, Galleys, March 2010.

M. Bacák, J.M. Borwein, A. Eberhard and B. Mordukhovich*“Infimal convolutions and Lipschitzian properties of subdifferentials for prox-regular functions in Hilbert spaces”, J. Convex Analysis, Special Volume in Honour of Hedi Attouch, Galleys July 2010.

D.H. Bailey* and J.M. Borwein“Exploratory experimentation and computation”, Notices of the AMS. Reprinted in Book Jonathan M. Borwein and Peter B. Borwein, Selected Writings on Experimental and Computational Mathematics, PsiPress, October 2010.

D.H. Bailey* and J.M. Borwein“High-precision numerical integration: Progress and challenges”, Journal of Symbolic Computation, D-drive Preprint 382, 2010.

D.H. Bailey*, J.M. Borwein and R.E. Crandall*“Advances in the theory of box integrals”, Mathematics of Computation, Vol.79, pp.1839-1866, 2010.

U. Baumgartner, G. Schlichting* and G. A. Willis“Geometric characterization of flat groups of automorphisms”, Groups, Geometry and Dynamics, Vol.4, pp.1-13, 2010.

E.J. Beh“Elliptical confidence regions for simple correspondence analysis”, Journal of Statistical Planning and Inference, Vol.140, pp.2582-2588, September 2010.

E.J. Beh“The aggregate association index”, Computational Statistics & Data Analysis, Vol.54, pp.1570–1580, June 2010.

E.J. Beh and L. D’Ambra*“Non-symmetrical correspondence analysis with concatenation and linear constraints”, The Australian and New Zealand Journal of Statistics, Vol.52, pp.27–44, 2010.


D.J. Best, J.C.W. Rayner and O. Thas*“Moment tests of fit for some discrete distributions with ecological applications”, International Journal of Ecological Economics & Statistics, Vol.17 (Special), Issue No.P10, pp.1-15, Spring 2010.

D.J. Best, J.C.W. Rayner and O. Thas*“Four tests of fit for the beta-binomial distribution”, Journal of Applied Statistics, Vol.37, Vol.9, pp.1547-1554, 2010.

J.M. Borwein“Fifty years of maximal monotonicity”, Optimization Letters, Vol.4 pp.473-490, 2010.

D. Borwein*, J.M. Borwein and I.E. Leonard*“Lp Norms and the sinc function”, American Math. Monthly, Vol.117, pp.528-539, June-July 2010.

J.M. Borwein and O-Y. Chan“Duality in tails of multiple zeta values”, Int. J. Number Theory, Vol.3, No.6, pp.501-514, 2010.

J.M. Borwein, O-Y. Chan and R. Crandall*“Higher-dimensional box integrals”, Experimental Mathematics. Vol.19, No.3, pp.431-446, 2010.

J.M. Borwein and R.E. Crandall*“Closed forms: what they are and why we care”, Notices Amer. Math. Soc, Reprinted in Book Jonathan M. Borwein and Peter B. Borwein, Selected Writings on Experimental and Computational Mathematics, PsiPress. October 2010.

J.M. Borwein and W.B. Moors“Stability of closedness of cones under linear mappings, II”, Journal of Nonlinear Analysis and Optimization: Theory & Applications, Galleys August 2010.

J.M. Borwein and S. Sciffer“An explicit non-expansive function whose subdifferential is the entire dual ball”, Technion Meeting on Nonlinear Analysis, AMS Proceedings in Contemporary Mathematics, Vol.514, pp.99-103, 2010.

J.M. Borwein and J. Vanderwerff“Fréchet-Legendre functions and reflexive Banach spaces”, J. Convex Analysis, Special Volume in honour of Hedy Attouch. Galleys June 2010, D-drive Preprint 398.

J.G. Cassey, R.A.R. King and P. Armstrong“Is there thermal benefit from preoperative warming in children?”, Pediatric Anesthesia, Vol.20, pp.63–71, 2010.

C. Chen, F. Liu, V.V. Anh and I.W. Turner“Numerical methods for solving a two-dimensional variable-order anomalous subdiffusion equation”, Mathematics of Computation, pp.1-21, 2010.

C. Chen, F. Liu, V.V. Anh and I.W. Turner“Numerical schemes with high Spatial accuracy for a variable-order anonolous subdiffusion Equations”, SIAM Journal on Computing, Vol.32, No.4, pp.1740-1760, 2010.

D.F. Coutinho*, M. Fu and C.E. de Souza*“Input and output quantized feedback linear systems”, IEEE Transactions on Automatic Control, Vol.55, No.3, pp.761-766, March 2010.

M. Falk, R. Denham and K. Mengersen“Estimating uncertainty in the revised universal soil loss equation via Bayesian melding”, J. Agricultural, Biological and Environmental Statistics, Vol. 15, No.1, pp.20-37. Online DOI: 10.1007/s13253-009-0005-y.

A.J. Fleming“Nanopositioning system with force feedback for high-performance tracking and vibration control”, IEEE Transactions on Mechatronics, Vol.15, No.3, pp.433-447 June, 2010.

A.J. Fleming “quantitative SPM topographies by charge linearization of the vertical actuator”, Review of Scientific Instruments, Vol.81, No.10, 103701(1-5) October, 2010,

A.J. Fleming, S.S. Aphale* and S.O.R. Moheimani“A new method for robust damping and tracking control of scanning probe microscope positioning stages “, IEEE Transactions on Nanotechnology, Vol. 9, No.4, pp.438-448, September, 2010,

A.J. Fleming, B.J. Kenton and K.K. Leang*“Bridging the gap between conventional and video-speed scanning probe microscopes”, Ultramicroscopy, Vol.110, No.9, pp.1205-1214. August, 2010,

A.J. Fleming and K.K. Leang*“Integrated strain and force feedback for high performance control of piezoelectric actuators”, Sensors and Actuators A, Vol.161, No.1-2, pp.256-265, June, 2010.

M. Fu and B.R. Barmish*“Robust output feedback stabilization for two new uncertainty structures”, IEEE Transactions on Automatic Control, Vol.55, No.1, pp.253-258, January 2010.

M. Fu and Lihua Xie*“quantized feedback control for linear uncertain systems”, Int. J. Robust and Nonlinear Control, Vol.20, No.8, pp.843-857, 2010.

D. Gajda*, C. Guihenneuc-Jouyaux*, J. Rousseau*, K. Mengersen and D. Nur“Use in practice of importance sampling for repeated MCMC for Poisson models”, Electronic Journal of Statistics, Vol.4, No.361–383, 2010.

P.J. Gawthrop*, B. Bhikkaji* and S.O.R. MoheimaniPhysical model based control of a piezoelectric tube for nano-scale positioning applications”, Mechatronics, Vol.20, No.1, pp.74–84, February 2010.

F. Ghahramani, C. J. Read and G.A. Willis“Closed ideal structure of cohomological properties of certain radical Banach algebras”, Proc. London Math. Soc., Vol.3, No.100, pp.533-539, May 2010.


H. Gloeckner and G.A. Willis“Classification of the simple factors appearing composition series of totally disconnected contraction”, J. Reine Angew. Math., Vol.643 pp.141-169, 2010.

K. Goebel* and B. Sims“Mean Lipschitzian mappings”, Contemporary Math., Vol.513, pp.157-167, 2010.

G.C. Goodwin, D.Q. Mayne*, K-Y Chen, C. Coates, G. Mirzaeva and D.E. Quevedo“An introduction to the control of switching electronic systems”, Annual Reviews in Control, Vol.34, No.2, pp.209-220, 2010.

G.C. Goodwin, E.I. Silva* and D.E. Quevedo“Analysis and design of networked control systems using the additive noise model methodology”, Asian Journal of Control, Vol.12, No.4, pp.443-459, 2010.

H. Haimovich* and M.M. Seron“Componentwise ultimate bound and invariant set computation for switched linear systems”, Automatica, Vol.6, No.11, pp.1897-1901.November 2010,

W. Hu, S. Tong, P. Dale and K. Mengersen“Difference in mosquito species (Diptera: Culicidae) and the transmission of Ross River Virus between coastline and inland areas in Brisbane, Australia”, Environmental Entomology, Vol. 39, No.1, pp.88-97, 2010.

W. Hu and K Mengersen“Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in queensland, Australia”, BMC Infectious Diseases, Vol. 10, p311, October 2010.

A. James, S. Low Choy and K. Mengersen“Elicitator: an expert elicitation tool for ecology, Environmental Modelling and Software, Vol.25, No.1, pp.129-145. Online http://dx.doi.org/10.1016/j.envsoft.2009.07.003.

S. Johnson, G. Hamilton, F. Fielding and K. Mengersen“An integrated Bayesian Network approach to Lyngbya majuscule bloom initiation”, Marine Environmental Research, Vol. 69, No.1, pp.27-37.

D.U. Keogh, J. Kelly, J. Mengersen, L. Morawska and R. Jayaratne“Derivation of motor vehicle particle emission factors for application to transport emission modelling”, J. Environmental Science and Pollution Research, Vol.17, No.3, pp.724-739. 10.1007/s11356-009-0210-9.

R. Lombardo and E.J. Beh“Simple and multiple correspondence analysis for ordinal-scale variables using orthogonal polynomials”, Journal of Applied Statistics, Vol.37, pp.2101–2116, December 2010.

C. Lovaas*, M.M. Seron, G.C. Goodwin“Robust output-feedback MPC with integral action”, IEEE Transactions on Automatic Control, Vol.55, No.7, pp.1531-1543, July 2010.

D. Marelli, M. Aramaki*, R. Kronland Martinet* and C. Verron*“Time-frequency synthesis of noisy Sounds with Narrow Spectral Components”, IEEE Transactions on Audio, Speech and Language Processing, Vol.18, No.8, pp.1929-1940, August 2010.

D. Marelli and P. Balazs*“On pole-zero vocal tract model estimation methods minimizing a logarithmic criterion”, IEEE Transactions on Audio, Speech and Language Processing, Vol.18, No.2, pp.237-248, February 2010.

D. Marelli and M. Fu“A recursive method for the approximation of LTI systems using subband processing”, IEEE Transactions on Signal Processing, Vol.58, No.3, pp.1025-1034, March 2010.

D. Marelli and M. Fu“A continuous-time linear system identification method for slowly sampled data”, IEEE Transactions on Signal Processing, Vol.58, No.5, pp.2521-2533, May 2010.

K. Mengersen“The sound of silence: listening to villagers to learn about orangutans”. Significance, Vol.7, pp.101-106, 2010.

R.H. Middleton* and J.H. Braslavsky“String instability in classes of linear time invariant formation control with limited communication range, IEEE Transactions on Automatic Control, Vol.55, No.7, pp.1519–1530, July 2010.

S.D. Mitchell and J.S. Welsh“The influence of complex permeability on the broadband frequency response of a power transformer”, IEEE Transactions on Power Delivery, Vol.25, pp.803-813, 2010.

T. Moffiet, J.D. Armston and K. Mengersen“Motivation, development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.65, pp.26–41, January 2010.

L. Moore, I.L. Hudson, E.J. Beh and D.G. Steel“Ecological inference techniques: An empirical evaluation using data describing gender and voter turnout at New Zealand elections, 1893-1919”. Journal of the Royal Statistical Society (Series A), Vol.173, pp.185–213, January 2010.

C. Ocampo-Martinez*, J.A. De Doná and M.M. Seron“Actuator fault-tolerant control based on set separation”, International Journal of Adaptive Control and Signal Processing, Online 28 Jun 2010, DOI: 10.1002/acs.1181.

C. Oldmeadow, K. Mengersen, J. Mattick and J. Keith“Multiple evolutionary rate classes in animal genome evolution”, Molecular Biology and Evolution,Vol.27, pp.942-953, 2010.

T. Perez and T.I. Fossen*“Practical aspects of frequency-domain identification of marine structures from hydrodynamic data”, Ocean Engineering, DOI: 10.1016/j.oceaneng.2010.11.004.


P. Rippon and J.C.W. Rayner“Generalised score and Wald tests”, Advances in Decision Sciences, Vol.2010, Article ID 292013, 8 pages, 2010. doi:10.1155/2010/292013.

C.R. Rojas*, M.B. Syberg*, J.S. Welsh and H. Hjalmarsson*“The cost of complexity in system identification: Frequency function estimation of finite impulse response systems”, IEEE Transactions on Automatic Control, Vol.55, No.10, pp. 2298-2309,October 2010.

M. Rolfe, K. Mengersen, G. Beadle, K. Vearcombe, B. Andrews, J. Johnson and C. Walsh“Latent class piecewise linear trajectory modelling for short-term cognition responses after chemotherapy for breast cancer patients”, J. Applied Statistics, March, Online DOI: 10.1080/02664760902729641.

M.E. Romero*, M.M. Seron and J.A. De Doná“Sensor fault tolerant vector control of induction motors”, IET Control Systems & Applications, Vol.4, Issue.9, pp.1707--1724, September 2010.

M.M. Seron and J.A. De Doná“Actuator fault tolerant multi-controller scheme using set separation based diagnosis”, International Journal of Control, Vol.83, No.11, pp. 2328—2339, November, 2010.

E.I. Silva*, G.C. Goodwin and D.E. Quevedo“Control system design subject to SNR constraints”, Automatica, Vol.46, No.2, pp.428-436, 2010.

B. Simonetti*, E.J. Beh and L. D’Ambra*“The analysis of dependence for three way contingency tables with ordinal variables: A case study of patient satisfaction”, Journal of Applied Statistics, Vol.37, pp.91–103, January 2010.

S.N. Snodgrass, D.A. Rivett, B.J. Robertson and E. Stojanovski“A comparison of cervical spine mobilization forces applied by experienced and novice physiotherapists”, Journal of Orthopaedic & Sports Physical Therapy, Vol.40, pp.392-401, April 2010.

C.M. Strickland, D.P. Simpson, I.W. Turner, R. Denham and K.L. Mengersen“Fast Bayesian analysis of spatial dynamic factor models for multi-temporal remotely sensed Imagery. Journal of the Royal Statistical Society C, Vol.60, No.1, pp. 1-16.

F. Tuyl, R. Gerlach and K. Mengersen “Consensus priors in the presence of general laws”, Journal of Applied Probability and Statistics, Vol.5, pp.31–42, May 2010.

N. Xiao*, L. Xie* and M. Fu“Stabilization of Markov jump linear systems using quantized state feedback”, Automatica, Vol.46, No.10, pp.1696-1702, October 2010.

Q. Yang, F. Liu and I.W. Turner“Numerical methods for fractional partial differential equations with Riesz space fractional derivatives”, Applied Mathematical Modelling, Vol.34, No.1, pp.200-218, 2010.

Q. Yang, F. Liu and I.W. Turner“Stability and convergence of an effective numerical nethod for the time-space fractional Fokker-Planck equation with a nonlinear source term”, International Journal of Differential Equations, 2010, pp.1-22, 2010.

A. Yetendje, M.M. Seron, J.A. De Doná and J.-J. Martinez*“Sensor fault-tolerant control of a magnetic levitation system”, International Journal of Robust and Nonlinear Control, Online: Apr 9, 2010. DOI: 10.1002/rnc.1572.

Y.K. Yong, S.O.R. Moheimani and Ian R. Petersen“High-speed cycloid-scan atomic force microscopy”, Nanotechnology, Vol.21, 2010. 365503 4pp.

Y.K. Yong, B. Ahmed, and S. O. R. Moheimani“Atomic force microscopy with a 12-electrode piezoelectric tube scanner”, Review of Scientific Instruments, Vol.81, No.3, 2010, 033701(10pp).

Y.K. Yong, K. Liu and S.O.R. Moheimani“Reducing cross-coupling in a compliant XY nanopositioning stage for fast and accurate raster scanning”, IEEE Transactions on Control Systems Technology, Vol.18, No.5, pp1172–1179, September 2010.

J. Zheng and M. Fu“A reset state estimator using an accelerometer for enhanced motion control with sensor quantization”, IEEE Transactions on Control Systems Technology, Vol.18, No.1. pp.79-90, January 2010.

J. Zheng, M. Fu, C. Du*, Y. Wang*, and L. Xie*“A factorization approach to sensitivity loop shaping for disturbance rejection in hard disk”, IEEE Transactions on Magnetics, Vol.46, No.5, pp.1220-1227, May 2010.

J. Zheng, W. Su* and M. Fu“Dual-stage actuator control design using a doubly coprime factorization approach”, IEEE/ASME Transactions on Mechatronics, Vol.15, No.3, pp.339-348, June 2010.

Y. Zhou, A. Bazaei, S.O.R. Moheimani and M. Yuce“A micromachined nanopositioner with on-chip electrothermal actuation and sensing” IEEE Electron Device Letters, Vol.31, No.10, pp.1161–1163, October 2010.

Y. Zhou, S.O.R. Moheimani and M. YuceUltrasonic energy transmission for implantable biosensors”, IEEE Electron Device Letters, Vol.3, No.4, pp.374–376, 2010.


JOURNAL PAPERS ACCEPTED FOR PUBLICATION

J.C. Agüero, G.C. Goodwin and P. Van den Hof*“A virtual closed loop identification method for closed loop identification”, to appear in Automatica.

J.C. Agüero, C. Rojas*, H. Hjalmarsson* and G.C. Goodwin“On the accuracy of Linear MIMO models”, to appear in Automatica.

M. Alamir*, J.S. Welsh and G.C. Goodwin“Synaptic plasticity-based dynamic model for epileptic seizures”, to appear in Automatica.

C. Alston, B. Farthing, A. Steven and K. Mengersen“Summarising and monitoring key characteristics of chlorophyll levels derived from satellite images”, to appear in Environmetrics.

A. Bazaei, S.O.R. Moheimani and A. Sebastian*“An analysis of signal transformation approach to triangular waveform tracking”, to appear in Automatica.

A. Bazaei*, Y.K. Yong, S.O.R. Moheimani and A. Sebastian*,“Tracking of triangular references using signal transformation for control of a novel AFM scanner stage”, to appear in IEEE Transactions on Control Systems Technology.

E.J. Beh, L. D’Ambra* and B. Simonetti*“Correspondence analysis of cumulative frequencies using a decomposition of Taguchi’s statistic”, to appear in Communications in Statistics – Theory and Methods.

E.J. Beh, R.Lombardo and B. Simonetti*“A European perception of food using two methods of correspondence analysis”, to appear in Food Quality & Preference.

E.J. Beh and E.J. Smith“Real world occupational epidemiology, Part 1: Odds ratios, relative risk and asbestos”, to appear in Archives of Environmental and Occupational Health.

E.J. Beh and E.J. Smith“Real world occupational epidemiology, Part 2: A visual interpretation of statistical significance”, to appear in Archives of Environmental & Occupational Health.

D.J. Best, J.C.W. Rayner and O. Thas*“Easily applied tests of fit for the Rayleigh distribution”, to appear in Sankhya B.

B. Bhikkaji*, S.O.R. Moheimani and I.R. Petersen“A negative imaginary approach to modeling and control of a collocated structure”, to appear in IEEE/ASME Transactions on Mechatronics.

B. De Boeck, O. Thas*, J.C.W. Rayner and D.J. Best“Smooth tests for the gamma distribution”, to appear in Journal of Statistical Computation & Simulation.

J. M. Borwein“The future of variational analysis”, to appear in Special issue of Set-valued Analysis in honour of Boris Mordukhovich’s 60th birthday.

J.M. Borwein, P. Howlett and J. Piantadosi“Copulas with maximum entropy”, to appear in Optimization Letters.

J.M. Borwein, A. Straub and J. Wan“Three-step and four-step random walks”, to appear in Experimental Mathematics.

J.M. Borwein and J. Vanderwerff“Constructions of uniformly convex functions”, to appear in Canadian Math. Bull.

M. Donald, S. Tose, J. Sidhu, C. Cook and K. Mengersen“Incorporating parameter uncertainty into quantitative microbial risk assessment (qMRA)”, to appear in J. Water and Health.

A. Earnest, J. Beard, G. Morgan and K. Mengersen“Small area estimation of sparse disease counts using shared component models – application to birth defect registry data in New South Wales, Australia”, to appear in Health and Place.

A. Esparza*, J.C. Agüero, C. Rojas* and B. Godoy“Asymptotic statistical analysis of some controller design strategies”, to appear in Automatica.

A. Feuer* and G.C. Goodwin“Online qualtization in nonlinear filtering”, to appear in Journal of Statistical Computation and Simulation.

A.J. Fleming“Dual-stage vertical feedback for high speed-scanning probe microscopy”, to appear in IEEE Transactions on Control Systems Technology, doi:10.1109/TCST.2010.2040282.

J. S. Freudenberg*, R.H. Middleton* and J.H. Braslavsky“Minimum variance control over a Gaussian communication channel, to appear in IEEE Transactions on Automatic Control,

S. Goater, A. Cook, A. Hogan, K. Mengersen, A. Hieatt and P. Weinstein“Strategies to strengthen public health inputs to water policy in response to climate change: an Australian perspective”, to appear in Asia-Pacific Journal of Public Health.

D. Gaida, C. Giuihenneuc-Jouyaux, J. Rousseau*, K. Mengersen and D. Nur“Use in practice for importance sampling for repeated MCMC for Poisson models”, accepted for publication in Electronic Journal of Statistics.

B. Godoy, G.C. Goodwin, J.C. Agüero, D. Marelli and T. Wigren*“Identification of FIR systems having quantized output data using a scenario-based EM algorithm”, to appear in Automatica.


G.C. Goodwin, A.M. Medioli, W. Sher, L.Vlacic and J.S. Welsh“Emulation-based virtual laboratories: a low-cost alternative to physical experiments in control engineering education”, to appear in IEEE Transactions on Education.

G.C. Goodwin, J.Yuz*, M. Salgado*, and J.C. Agüero“Variance or spectral density in sampled data filtering?”, to appear in Journal of Global Optimization.

H. Haimovich*, J.H. Braslavsky and F.E. Felicioni*“On feedback stabilisation of switched discrete-time systems via Lie-algebraic techniques”, Provisionally accepted in the IEEE Transactions on Automatic Control.

J.A. Hogan, S. Izu* and J.D. Lakey“Sampling approximations for time – and bandlimiting, to appear in Journal of Sampling Theory in Signal and Information Processing,

C. Holden*, T.I. Fossen* and T. Perez“A Lagrangian approach to nonlinear modeling of anti?roll tanks”, to appear in Journal of Ocean Engineering.

F.C. Jarrad, S. Barrett, J. Murray, J. Parkes, R. Stoklosa, K. Mengersen and P. Whittle“Improved design method for biosecurity surveillance and early detection of non-indigenous rats”, to appear in New Zealand Journal of Ecology, Special Issue Vol.34, No.4, http://www.nzes.org.nz/nzje/new_issues/NZJEcol_JarradIP.pdf

F. Jarrad, P. Whittle, J. Murray, S. Barrett and K. Mengersen“Ecological aspects of biosecurity surveillance design for the detection of multiple invasive animal species”, to appear in Biological Invasions.

R. Lombardo, E.J. Beh and A. D’Ambra*“Studying the dependence between ordinal-nominal categorical variables via orthogonal polynomials”, to appear in Journal of Applied Statistics.

I.A. Mahmoud, S.O.R. Moheimani and B. Bhikkaji*“A new scanning method for fast atomic force microscopy”, to appear in IEEE Transactions on Nanotechnology.

A. Morton, M. Waterhouse, D. Cook and K. Mengersen“Limiting risk of hospital adverse events: avoiding train wrecks is more important than counting and reporting them”, to appear in J. Hospital Infection.

C. Müeller, D.E. Quevedo and G.C. Goodwin“How good is quantized model predictive control with horizon one?”, to appear in IEEE Transactions on Automatic Control.

R. Nazari, M.M. Seron and A. Yetendje“Invariant-set-based fault tolerant control using virtual sensors”, to appear in IET Control Theory and Applications.

S. Olaru*, J.A. De Doná, M.M. Seron and F. Stoican*“Positive invariant sets for fault tolerant multisensory control schemes”, to appear in International Journal of Control.

D. Parienta, L. Morawska, G.R. Johnson, Z.D. Ristovski, M. Hargreaves, K. Mengersen, S. Corbett, C.Y.H. Chao, Y. Li and D. Katoshevski“Theoretical analysis of the motion and evaporation of exhaled respiratory droplets of mixed composition”, to appear in J. Aerosol Science.

E. Pereira*, S. Aphale*, V. Feliu* and S.O.R. Moheimani“Integral resonant control for vibration damping and precise tip-positioning of a single-link flexible manipulator”, to appear in IEEE/ASME Transactions on Mechatronics, Vol.16, No.2, pp.232–240, April 2011.

T. Perez and T.I. Fossen*“Practical aspects of frequency?domain identification of dynamic models of marine structures from hydrodynamic data”, to appear in Journal of Ocean Engineering.

M. Rolfe and K. Mengersen“Bayesian estimation of extent of recovery for aspects of verbal memory in women undergoing adjuvant chemotherapy treatment for breast cancer”, to appear in J. Royal Statistical Society Series A.

C.R. Rojas*, J.C. Agüero, J.S. Welsh, G.C. Goodwin and A. Feuer*“Robustness in experiment design”, to appear in IEEE Transactions on Automatic Control.

A. Salton, Z. Chen, J. Zheng and M. Fu“Preview control of dual-stage actuator systems for super fast transition time”, to appear in IEEE/ASME Transactions on Mechatronics.

M.M. Seron, J.A. De Doná and J. Richter*“Fault tolerant control using virtual actuators and set-separation detection principles”, to appear in International Journal of Robust and Nonlinear Control.

D.R. Smith and E.J. Beh“Real world occupational epidemiology: Irving Selikoff, odds ratios and asbestos (Editorial)”, to appear in Archives of Environmental and Occupational Health.

M. Stanaway, R. Reeves and K. Mengersen“Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions”, to appear in J. Environmental and Ecological Statistics, pp.1-23-23.

K. Stockton, K. Mengersen, J. Paratz, D. Kandiah and K. Bennell“Effect of vitamin D supplementation on muscle strength: a systematic review and meta-analysis”, to appear in Osteoporosis International, pp. 1-13.

C. Strickland, D. Simpson, I. Turner, R. Denham and K. Mengersen“Fast Bayesian analysis of spatial dynamic factor models for multi-temporal remotely sensed imagery”, to appear in Computational Statistics.


F. Suryawan, J.A. De Doná and M.M. Seron“Splines and polynomial tools for flatness-based constrained motion planning”, to appear in International Journal of Systems Science.

F. Tuyl“Comment on “Integrated objective Bayesian estimation and hypothesis testing” by Bernardo, J. M.”, to appear in Bayesian Statistics 9, Oxford University Press.

L. Wang, L. Morawska, E.R. Jayaratne, K. Mengersen and D. Heuff“Characteristics of airborne particles and the factors affecting them at bus stations”, to appear in Atmospheric Environment.

M. Waterhouse, H. Assareh, I. Smith and K. Mengersen“Implementation of multivariate control charts in a clinical setting”, to appear in International Journal for Quality in Health Care.

A. Yetendje, J.A. De Doná and M.M. Seron“Multisensor fusion fault tolerant control”, to appear in Automatica.

K. You*, W. Su*, M. Fu and L. Xie*“Attainability of the minimum data rate for stabilization of linear systems via logarithmic quantization”, to appear in Automatica.

W. Yu, P. Vaneckova, K. Mengersen, X. Pan and S. Tong“Is the association between temperature and mortality modified by age, gender and socio-economic status?”, to appear in Science of the Total Environment.

J. Yuz*, J. Alfaro*, J.C. Agüero and G.C. Goodwin“Identification of continuous-time state space models from nonuniform fast-sampled data”, to appear in IEE/TCT.

Y. Zhu, S.O.R. Moheimani and M. Yuce“A 2-DO F MEMS ultrasonic energy harvester”, to appear in IEEE Sensors Journal, Vol.11, No.1, pp.155-161, January 2011.

REFEREED CONFERENCE PAPERS

J.C. Agüero, J.I. Yuz*, G.C. Goodwin and W. Tang*“Identification of state-space systems using a dual time-frequency domain approach”, Proc. 49th IEEE Conference on Decision and Control, (CDC), Atlanta, Georgia USA, 15-17 December, 2010.

C. Alston”Bayesian mixture models: A blood free dissection of a sheep”, Mixture Estimation and Applications, Edinburgh, 3-5 March, 2010.

C. Alston and K. Mengersen“Bayesian Normal Mixture Modelling: An examination via Case Studies”, Statistical Modelling and Inference Conference to celebrate Murray Aitkin’s 70th birthday, qUT Gardens Point, Brisbane, queensland, Australia, 1-4 February 2010.

C. Alston and K. Mengersen“Bayesian Mixture Models for Monitoring Reef Health”, Ninth Valencia International Meeting on Bayesian Statistics 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm Alicante, Spain, 3-8 June 2010.

A. Bazaei, S.O.R. Moheimani and A. Sebastian*“Stability of signal transformation method for triangular waveform tracking”, Proc. 49th IEEE Conference on Decision and Control, Atlanta, GA, USA, December 2010.

A. Bazaei, Y.K. Yong and S.O.R. Moheimani“Tracking control of a novel AFM scanner using signal transformation method”, Proc. 5th IFAC Symposium on Mechatronic Systems, Cambridge, MA, USA, 13-15 September 2010.

E.J. Beh and B. Simonetti*“A few moments for non-symmetrical correspondence analysis”, Proc. Agrostat 2010 – 11th European Symposium on Statistical Methods for the Food Industry, Benevento Italy, pp.277–286, 24-26 February 2010.

J. Borwein“Douglas-Ratchford iterations in the absence of convexity”, ANZIAM-SigmaOpt Session, queenstown New Zealand, 31 January-4 February 2010.

J. Borwein“Why Convex?” Colloquium, Universitaet der Bundeswehr Munchen, March 2010.

J. Borwein“Compressed sensing: a subgradient approach” Special session on Optimization, 54th Australian Mathematical Society Annual Meeting, The University of queensland, Brisbane, 27-30 September 2010.

E.J. Carr, I.W. Turner and P. Perré“A mass conservative mesoscopic dyring model”, Proceedings of the 17th International Drying Symposium, Magdeburg, Germany, pp.285-292, 3-6 October 2010.

M.G. Cea, G.C. Goodwin and A. Feuer*“A discrete nonlinear filter for fast sampled problems based n vector quantization”, Proc. American Control Conference (ACC2010), Baltimore, USA, 30 June-2 July 2010.

L. Chai* and M. Fu“Infinite horizon LqG control with fixed-rate quantization for scalar systems”, 8th World Congress on Intelligent Control and Automation (WCICA), Jinan, Shandong China, pp.894-899, 7-9 July 2010.

P. de Lamberterie, B. Godoy and J.S. Welsh“Extremum seeking control applied to a model of the Hall-Heroult aluminium process”, Proc. UKACC International Conference on Control, Coventry, UK, September, 2010.

M. Donald, C. Alston, R. Young and K. Mengersen“Bayesian analysis of complex agricultural field trials”, Statistical Modelling and Inference Conference to celebrate Murray Aitkin’s 70th birthday, qUT Gardens Point, Brisbane, queensland, Australia 1-4 February 2010.


M. Donald, C. Alston, R. Young and K. Mengersen“Bayesian analysis of complex agricultural field trials”, Ninth Valencia International Meeting on Bayesian Statistics 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm, Alicante, Spain, 3-8 June 2010.

Y. Diao*, M. Fu and H. Zhang*“An overview of range detection techniques for wireless sensor networks”, 8th World Congress on Intelligent Control and Automation (WCICA), Jinan, Shandong China pp.1150-1155, 7-9 July 2010.

A.A. Eielsen* and A.J. Fleming“Passive shunt damping of a piezoelectric stack nanopositioner, (Invited Session), Proc. American Control Conference (ACC2010), Baltimore, USA, 30 June-2 July 2010.

A.J. Fleming“Ultra-fast dual-stage vertical positioning for high performance SPMs” (Invited Session), Proc. American Control Conference (ACC2010), Baltimore, USA, 30 June-2 July 2010.

A.J. Fleming“High speed nanopositioning with force feedback, (Invited Session), Proc. American Control Conference (ACC2010), Baltimore, USA, 30 June-2 July 2010.

A.J. Fleming and K.K. Leang*“High performance nanopositioning with integrated strain and force feedback”, (Invited Session), Proc. IFAC Symposium on Mechatronic Systems, Boston, MA, pp.117-124, September, 2010.

G.C. Goodwin“Virtual closed loop systems identification”, Workshop dedicated to Brian Anderson, 49th IEEE Conference on Decision and Control (CDC2010), Atlanta, Georgia, USA, December 15-17, 2010.

G.C. Goodwin, M.G. Cea and A. Feuer*,“Rapprochement between discrete and continuous nonlinear filtering”, Proc. American Control Conference (ACC2010), Baltimore, USA, 30 June-2 July 2010..

G.C. Goodwin, M. Cea, K. Lau and T. Wigren*“Control challenges in mobile telecommunications”, Report hosted on the IEEE Control Systems Society web-site, 16 October 2010.

G.C. Goodwin, J.I. Yuz*, J.C. Agüero and M. Cea“Sampling and sampled-data models”, Proc. American Control Conference (ACC2010), Baltimore, USA, 30 June-2 July 2010.

H. Haimovich* and J.H. Braslavsky“Feedback stabilisation of switched systems via iterative approximate eigenvector assignment, Proc. 49th IEEE Conference on Decision and Control (CDC), Atlanta, USA, 2010.

S. Halat*, J.I. Yuz* and J.C. Agüero“On frequency-domain maximum likelihood identification of state-space time-varying systems”, UKACC International Conference on Control, Coventry, 7-10 September 2010.

C. Han*, H. Zhang* and M. Fu“Linear estimation for discrete-time systems with Markov jump delays”, 8th World Congress on Intelligent Control and Automation (WCICA), Jinan, Shandong China, pp.981-987, 7-9 July 2010.

J. Hogan“Hypercomplex Fourier and wavelet transforms”, Workshop on Optimal Frames and Operator Algebras, San Francisco State University, January 2010.

J. Hogan“The Clifford-Fourier transform”, 54th Australian Mathematical Society Annual Meeting, The University of queensland, Brisbane, 27-30 September 2010.

K. Hong, S.K. Chalup and R.A.R. King“A component based approach improves classification of discrete facial expressions over a holistic approach”, Proc. 2010 International Joint Conference on Neural Networks (IJCNN 2010, part of IEEE WCCI), Barcelona, pp.90–97, 18-23 July 2010.

L. Ji, Y. Zhu, S.O.R. Moheimani and M. Yuce“A micromachined 2-DOF nanopositioner with integrated capacitive displacement sensor”, Proc. IEEE Sensors 2010 Conference, Hawaii, USA, 1-4 November 2010.

S. Johnson and K. Mengersen“Object Oriented Bayesian Networks Designing for Simplicity and Integration”, Ninth Valencia International Meeting on Bayesian Statistics 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm, Alicante, Spain, 3-8 June 2010.

S. Kuiper*, A.J. Fleming and G. Schitter“Dual actuation for high-speed atomic force microscopy”, (Invited Session), Proc. IFAC Symposium on Mechatronic Systems, Boston, MA, pp.220-226, 13-15 September, 2010.

J. Kulk and J.S. Welsh“Autonomous optimisation of joint stinesses over a gait cycle for the NAO robot”, Proc. International Symposium on Robotics and Intelligent Sensors, Nagoya, Japan, March, 2010.

J. Kulk and J.S. Welsh“Perturbation sensing for humanoid robots using a multiclass support vector machine”, Proc. IFAC Symposium on Mechatronic Systems, Cambridge, Massachusetts, USA, September, 2010.

S. J. Low-Choy, K. Mengersen, J.V. Murray and J. Rousseau*“How Should We Combine Expert Opinions: On Elicitation, Encoding, Priors or Posteriors?” Ninth Valencia International Meeting on Bayesian Statistics 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm, Alicante, Spain, 3-8 June 2010.

I.A. Mahmood and S.O.R. Moheimani“Spiral scanning: An alternative to conventional raster scanning in high-speed scanning probe microscopes”, Proc. American Control Conference, Baltimore, MD, USA, July 2010.


I.A. Mahmood and S.O.R. Moheimani“Spiral-scan atomic force microscopy: A constant linear velocity approach” Proc. 10th IEEE International Conference on Nanotechnology, Seoul, Korea, 17-20 August 2010

D. Marelli, M. Aramaki*, R. Kronland-Martinet* and C. Verron*“A unified time-frequency method for synthesizing noisy sounds with short transients and narrow spectral components”, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, Texas, USA, 14-19 March, 2010.

D. Marelli, B. Godoy and G.C. Goodwin“Parameter estimation in state-space models having quantized output data”, Proc. 49th IEEE Conference on Decision and Control, (CDC), Atlanta, Georgia USA, 15-17 December, 2010.

K. Mengersen and N. White“Bayesian Mixtures: Past, Present and Potential”, Statistical Modelling and Inference Conference to celebrate Murray Aitkin’s 70th birthday, qUT Gardens Point, Brisbane, queensland, Australia, 1-4 February 2010.

G. Mirzaeva, J.S. Welsh, R.E. Betz and T.J. Summers“Frequency analysis of experimental waveforms for DC motors in digging applications”, Proc. 2010 IEEE-Industry Applications Society Conference, Houston, Texas, USA, October, 2010.

G. Mirzaeva, J.S. Welsh, R.E. Betz and T.J. Summers“Experimental investigation of frequency characteristics of large industrial DC machines with thyristor-based drives”, AUPEC 2010, Australasian Universities Power Engineering Conference, Christchurch, New Zealand, December 2010.

S.D. Mitchell, J.S. Welsh and G.H.C. Oliveira*“Physical modelling of power transformers utilising end to end frequency response analysis”, CIGRE International Workshop on Power Transformers, VI Workspot, Foz do Iguacu, Brazil, April 2010.

M. Nacusse*, M. Romero*, H. Haimovich* and M.M. Seron“DTFC versus MPC for induction motor control reconfiguration after inverter faults”, 2010 Conference on Control and Fault Tolerant Systems, Nice, France, 6-8 October 2010.

R. Nazari, A. Yetendje and M. Seron“Fault-tolerant control of a magnetic levitation system using virtual sensor”, 18th Mediterranean Conference on Control and Automation, Congress Palace, Marrakech, Morocco, 23-25 June 2010.

G.H.C. Oliveira*, F.F. Schauenburg*, R. Maestrelli*, A.C.O. Rocha*, S.D. Mitchell and J.S. Welsh“SimPowerSystems for the study of transients in the Irapé-Araçuaí system with the aim of using black box transformer models”, CIGRE International Workshop on Power Transformers, VI Workspot, Parana, Brasil, (2010).

G.H.C. Oliveira*, S.D. Mitchell and J.S. Welsh“Modeling approaches based on FRA tests for simulating and analyzing a power transformer’s dynamic behavior: A comparison”, ARWtr 2010, Advanced Research Workshop on transformers, Santiago de Compostela, Spain, October 2010.

T. Perez and M. Blanke*“Ship roll motion control”, 8th IFAC Conference on Control Applications in Marine Systems, September. Rostock, Germany, 15-17 September 2010.

T. Perez, P. de Lamberterie, A. Donaire and E. Revestido-Herrero*“Parameter estimation of thrust models of uninhabited vehicles and systems”, 7th IFAC Symposium on Intelligent Autonomous Vehicles, Lecce, Italy, 6-8 September 2010.

T. Perez and A. Donaire“Port Hamitonian theory of motion control for marine craft”, 8th IFAC Conference on Control Applications in Marine Systems, Rostock, Germany, 15-17 September 2010.

T. Perez and A. Donaire“Control design for positioning of underwater vehicles with control allocation”, Pacific International Maritime Conference, Sydney, Australia, 26-28 January 2010.

T. Perez, A. Donaire and B. Williams“On evaluation of robust autonomy of uninhabited vehicles and systems”, 7th IFAC Symposium on Intelligent Autonomous Vehicles, Lecce, Italy, 6-8 September. 2010.

T. Perez and E. Revestido Herrero*“Damping structure selection in nonlinear ship manoeuvring models”, 8th IFAC Conference on Control Applications in Marine Systems, Rostock, Germany, 15-17 September 2010.

C. Perfumo, J. K. Ward and J. H. Braslavsky“Achieving a synergy by combining air conditioners and photovoltaic panels: an evolutionary-algorithm approach”, Proc. 20th Australasian Universities Power Engineering Conference (AUPEC): Power Quality for the 21st Century, Christchurch, New Zealand, 5-8 December 2010.

C. Perfumo, J.K. Ward and J. H. Braslavsky“Reducing energy use and operational cost of air conditioning systems with multi-objective evolutionary algorithms”, Proc. IEEE Congress on Evolutionary Computation, Barcelona, 18-23 July 2010.

R. Remond, I.W. Turner and P. Perré“Dual-scale model for heat treatment of wood: evidence of thermal run-away due to the cumulative effect of exothermic reactions”, Proc. 17th International Drying Symposium, Magdeburg, Germany, pp.1311-1316, 3-6 October 2010.


E. Rohr, D. Marelli and M. Fu“Statistical properties of the error covariance in a Kalman Filter with random measurement losses”, Proc. 49th IEEE Conference on Decision and Control, (CDC), Atlanta, Georgia USA, 15-17 December, 2010.

E. Rohr, L.F.A. Pereira* and D.F. Coutinho*“Local stability analysis of feedback linearizing schemes for a class of uncertain nonlinear systems”, 8th IFAC Symposium on Nonlinear Control Systems, Bologna, Italy, 1-3 September, 2010.

M.E. Romero* and M.M. Seron“New speed-sensorless control of induction motors with improved fault tolerance against current sensor failure”, 18th Mediterranean Conference on Control and Automation, Congress Palace, Marrakech, Morocco, 23-25 June 2010.

M. Romero* and M.M. Seron“Switching strategy for sensor fault tolerant vector control of doubly fed induction machines”, 2010 Conference on Control and Fault Tolerant Systems, Nice, France, 6-8 October 2010

B. Sims“Developing nonlinear analysis in CAT(0)-spaces”, 54th Australian Mathematical Society Annual Meeting, The University of queensland, Brisbane, 27-30 September 2010.

F. Stoican*, S. Olaru*, J.A. De Doná and M.M Seron“Improvements in the sensor recovery mechanism for a multisensor control scheme”, Proc. American Control Conference, Baltimore, Maryland, USA, 30 June-2 July, 2010.

F. Stoican*, S. Olaru*, M.M. Seron and J.A. De Doná“Reference governor for tracking with fault detection capabilities”, 2010 Conference on Control and Fault Tolerant Systems, Nice, France, 6-8 October 2010.

F. Stoican*, S. Olaru*, M.M. Seron and J.A. De Doná“A fault tolerant control scheme based on sensor-actuation channel switching and dwell time”, Proc. 49th IEEE Conference on Decision and Control, (CDC), Atlanta, Georgia USA, 15-17 December, 2010.

F. Suryawan, J.A. De Doná and M.M. Seron“On splines and polynomial tools for constrained motion planning” 18th Mediterranean Conference on Control and Automation, Congress Palace, Marrakech, Morocco, 23-25 June 2010.

F. Suryawan, J.A. De Doná and M.M. Seron“Methods for trajectory generation in a magnetic-levitation system under constraints” 18th Mediterranean Conference on Control and Automation, Congress Palace, Marrakech, Morocco, 23-25 June 2010.

F. Suryawan, J.A. De Doná and M.M. Seron“Fault detection, isolation, and recovery using spline tools and differential flatness with application to a magnetic levitation system”, 2010 Conference on Control and Fault Tolerant Systems, Nice, France, 6-8 October, 2010.

I.W. Turner, M. Ilic and P. Perré“The use of fractional-in-space diffusion equations for describing microscale diffusion in porous media”, Proc. 17th International Drying Symposium, Magdeburg, Germany pp.432-437, 3-6 October 2010.

C. Venkatesh and S.O.R. Moheimani“Experimental study of nonlinear characteristics of a MEMS resonator”, Proc. 5th Asia-Pacific Conference on Transducers and Micro-Nano Technology, Perth, Western Australia, 6-9 July, 2010.

M. Waterhouse, A.Morton, K. Mengersen and D. Looke“Using Bayesian networks to model the incidence of Methicillin-resistant Staphylococcus aureus colonisation”, Statistical Modelling and Inference Conference to celebrate Murray Aitkin’s 70th birthday, qUT Gardens Point, Brisbane, queensland, Australia, 1-4 February 2010.

Li Wei*, H. Zhang* and Minyue Fu“Finite-horizon quantized estimation using sector bound approach”, 8th World Congress on Intelligent Control and Automation (WCICA), Jinan, Shandong China, pp.900-903, 7-9 July 2010.

G. Willis“Totally disconnected locally compact groups”, CAT(0)-Spaces and Affine Buildings, Sde-Boker Israel, 31 January-6 February 2010.

G. Willis“Automorphisms of totally disconnected compact groups I & II”, Non-Archimedean Analysis, Lie Groups and Dynamical Systems, University of Paderborn, February 2010.

G. Willis“Commensurators of arithmetic groups”, Mathematics Colloquium, University of Milano-Bicocca, February 2010.

G. Willis“Contraction groups in locally compact groups”, 54th Australian Mathematical Society Annual Meeting, The University of queensland, Brisbane, 27-30 September 2010.

A. Yetendje, M.M. Seron and J.A. De Doná“Multisensor fusion fault-tolerant control of a magnetic levitation system”, 8th Mediterranean Conference on Control and Automation, Congress Palace, Marrakech, Morocco, 23-25 June 2010.


A. Yetendje, M.M. Seron and J.A. De Doná“Robust MPC design for fault tolerance of constrained multisensor linear systems”, 2010 Conference on Control and Fault Tolerant Systems, Nice, France, 6-8 October 2010.

Y.K. Yong, B. Arain* and S.O.R. Moheimani“A 12-electrode piezoelectric tube scanner for fast atomic force microscopy”, Proc. American Control Conference, Baltimore, MD, USA, July 2010.

Y.K. Yong and S.O.R. Moheimani“A compact XYZ scanner for fast atomic force microscopy in constant force contact mode”, Proc. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Montreal, Canada, 6-9 July 2010.

J.I. Yuz*, J. Alfaro*, J.C. Agüero and G.C Goodwin“EM identification of continuous-time state space models from fast sampled data”, 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010), Budapest, Hungary, 5-9 July 2010.

J. Zheng and M. Fu“A unified dual-stage actuator control scheme for track seeking and following in hard disk drives”, Proc. 8th IEEE International Conference on Control and Automation, Xiamen, China, 9-11 June 2010.

Y. Zhu, S.O.R. Moheimani and M. Yuce“A MEMS nanopositioner with thermal actuator and on-chip thermal sensor”, Proc. IEEE Sensors 2010 Conference, Hawaii, USA, 1-4 November 2010.

TECHNICAL REPORTS TO INDUSTRY:

BHP Billiton InnovationBHPB/IMP/10/1 B. Godoy and G.C. Goodwin “Parameterization of the Schwartz-Smith Model using Real Copper Data” January 2010

BHPB/IMP/10/2 M.M. Zhang “A Preliminary Study on Mining Phase Design with Simultaneous Production Scheduling using MILP” 23 June 2010

BHPB/OBOG/10/1 K. Lau and J.H. Braslavsky “Adaptive Cancellation of Power Line Interference in Total Field Measurements (Stuka Data, May 2008)” 19 February 2010

BHPB/OBOG/10/2 K. Lau and J.H. Braslavsky “Improved Methods of Adaptive Interference Cancellation (Stuka Data, May 2008)” 14 May 2010

BHPB/OBOG/10/3 K. Lau and J.H. Braslavsky “Adaptive Interference Cancellation – Further Results (Stuka Data, May 2008)” 15 November 2010

Boeing Research and Technology AustraliaBoeing/RobustAutonomy/10/1 T. Perez and A. Donaire “Operational Performance Based on Manoeuvring Criteria and its Use in Robust Autonomy Evaluation” February 2010

Boeing/RobustAutonomy/10/2 T. Perez, P de Lamberterie and A. Donaire “Parameter Estimation of Thrust Models for Autonomous Uninhabited Airborne Vehicles” February 2010

Boeing/RobustAutonomy/10/3 T. Perez “UAS Operational Performance Assessment: Progress and Research Outlook” July 2010

CFW Hamilton JetCFW-HJ/10/1 T. Perez Confidential Report July 2010

CSRCSR/Brake/10/1 C. Stuart “CSR Brake Van Control” February 2010

DSTODSTO/PHS/DP/10/1 A. Donaire and T. Perez “An Initial Study on the Use of Port-Hamiltonian Theory for Control of Marine Craft” April 2010

DSTO/PHS/DP/10/2 A. Donaire and T. Perez “Port-Hamilton control for dynamic positioning of marine craft with input constraints” October 2010

DSTO/PHS/DP/10/3 A. Donaire and T. Perez “Integral Control of Port Hamiltonian Systems Using Immersion and Invariance” December 2010

EnergyAustraliaEnergyAust/FaultDetect/10/1 S.D. Mitchell, J.S Welsh and J. De Doná “Physical Modelling of Power Transformers Utilising End to End Frequency Response Analysis” April 2010

EnergyAust/FaultAccom/10/1 G.J. Adams, J.H. Braslavsky and M.M. Seron “EA Fault Accommodation – Analysis of Croudace Bay Zone Substation Voltage Regulation Data” 16 July 2010


EnergyAust/FaultAccom/10/2 G.J. Adams, J.H. Braslavsky and M.M. Seron “Fault Accommodation in Electricity Networks Preliminary Model and Simulations of the Voltage Regulation Scheme at Croudace Bay Zone Substation” 30 September 2010

EnergyAust/FaultAccom/10/3 G.J. Adams, J.H. Braslavsky and M.M. Seron “Fault Accommodation in Electricity Networks – Utilisation of Distribution Monitoring and Control Data in Voltage Regulation (Croudace Bay Data)” 1 October 2010

EnergyAust/FaultAccom/10/4 G.J. Adams, M.M. Seron and J.A. De Doná “EA Fault Accommodation – Analysis of Croudace Bay VR Data, and Modelling of the Pelican Network” 17 December 2010

Ericsson ABEricsson/10/1 G.C. Goodwin and K. Lau Confidential Report #11 15 February 2010

Ericsson/10/2 K. Lau and G.C. Goodwin Confidential Report #12 23 February 2010

Ericsson/10/3 K. Lau and G.C. Goodwin Confidential Report #13 9 March 2010

Ericsson/10/4 K. Lau and G.C. Goodwin Confidential Report #14 15 March 2010

Ericsson/10/5 K. Lau and G.C. Goodwin Confidential Report #15 12 April 2010

Ericsson/10/6 M. Cea and G.C. Goodwin Confidential Report #16 30 April 2010

Ericsson/10/7 M. Cea and G.C. Goodwin Confidential Report #17 6 May 2010

Ericsson/10/8 G.C. Goodwin and K. Lau Confidential Report #18 17 May 2010

Ericsson/10/9 K. Lau and G.C. Goodwin Confidential Report #19 17 June 2010

Ericsson/10/10 K. Lau and G.C. Goodwin Confidential Report #20 12 July 2010

Ericsson/10/11 K. Lau and G.C. Goodwin Confidential Report #21 13 September 2010

Ericsson/10/12 D.S. Carrasco, G.C. Goodwin and K. Lau Confidential Report #22 13 September 2010

Ericsson/10/13 K. Lau and G.C. Goodwin Confidential Report #23 19 October 2010

Ericsson/10/14 K. Lau and G.C. Goodwin Confidential Report #24 19 October 2010

Ericsson/10/15 K. Lau and G.C. Goodwin Confidential Report #25 14 December 2010

Hatch-IASHatchIAS/10/1 G.C. Goodwin, T. Gustafsson* and A. Rojas “A Summary on the Coating Mass Process and its Current Control Solution” 27 January 2010

IBMIBM/10/1 A. Bazaei, Y.K. Yong and S.O.R. Moheimani “Tracking Control of a Novel AFM Scanner using Signal Transformation Method” January 2010

IBM/10/2 A. Bazaei and S.O.R. Moheimani “Tracking Control of Arbitrary References by Signal Transformation” 18 March 2010

IBM/10/3 A. Bazaei and S.O.R. Moheimani “Signal Transformation Approach to Tracking Control of Arbitrary References” 3 May 2010

MatrikonMat/NGMT/10/1 G.J. Adams “Next Generation Model-Based Control Tools – Progress Report for August 2010” 19 August 2010

Mat/NGMT/10/2 G.J. Adams and A. Medioli “Next Generation Model-Based Control Tools – Robust Control for CPOmpc” 23 August 2010

Mat/NGMT/10/3 A. Medioli “Next Generation Model-Based Control Tools – PID to MPC Tuning” 26 October 2010

Mat/NGMT/10/4 G.J. Adams “Next Generation Model-Based Control Tools – Progress Report for November 2010” 25 November 2010

Mat/ADCC/10/1 A. Medioli “Automated Downtime Cause Classifier – Report July 2010” 29 July 2010

Offshore Simulator CentreOSC/PHC/10/1 T. Perez and A. Donaire “Low-Speed Positioning Control Under Different Propulsion Configurations” 6 August 2010

OSC/PHC/10/2 T. Perez “Wave Filtering Within a Simulation Environment” 6 August 2010

OSC/PHC/10/3 T. Perez and A. Donaire “Low-Speed Positioning Control Under Different Propulsion Configurations – Part B” 6 August 2010


pERfoRMANCE INDICAToRS

Description2008

Actual2009

Actual2010

ActualDetails of 2010 Outcomes

2008-2010 Target

Refereed International Journal & Conference Publications

J: 57 C: 59

J: 68 C: 67

J: 83 C: 84

See Publications 150

Number of Patents 1 6 2 See Publications 2

Invitations to address and participate in international conferences

17 10 27 J. C. Agüero, J. I. Yuz*, G. C. Goodwin and W. Tang*“Identification of state-space systems using a dual time-frequency domain approach”, (Invited Session), Proc. 49th IEEE Conference on Decision and Control, Atlanta, December 2010.

C. AlstonInternational Society Bayesian Analysis World Meeting, Spain

A.A. Eielsen* and A.J. Fleming“Passive shunt damping of a piezoelectric stack nanopositioner”, (Invited Session), Proc. American Control Conference, Baltimore, MD, June 2010.

A.J. Fleming“Ultra-fast dual-stage vertical positioning for high performance SPMs” (Invited Session), Proc. American Control Conference Baltimore, MD, June, 2010.

A.J. Fleming“High speed nanopositioning with force feedback, (Invited Session), Proc. American Control Conference, Baltimore, MD, pp.4969-4974, June, 2010.

A.J. Fleming and K.K. Leang*“High performance nanopositioning with integrated strain and force feedback”, (Invited Session), Proc. IFAC Symposium on Mechatronic Systems, Boston, MA, pp.117-124, September, 2010.

G.C. Goodwin“Virtual closed loop system identification”, (Invited Presentation), Half-Century’s Excellence in Systems and Control Engineering, Atlanta, USA, 14 December.

S. Kuiper*, A.J. Fleming, and G. Schitter“Dual actuation for high-speed atomic force microscopy”, (Invited Session), Proc. IFAC Symposium on Mechatronic Systems, Boston, MA, September, 2010.

K. MengersenMixture estimation and applications (Invited speaker, Keynote Presentation, Proceedings (editor, Organiser); International Society Bayesian Analysis World Meeting, Spain

J. I. Yuz*, , J. Alfaro*, J. C. Agüero, and G. C Goodwin“EM identification of continuous-time state space models from fast sampled data”, (Invited Session), 19th International Symposium on Mathematical Theory of Networks and Systems (MTNS), 2010.

See also “Plenary and Keynote Addresses” in Publications.

15

RESEARCH FINDINGS AND COMPETITIVENESS


Description2008

Actual2009

Actual2010


2008-2010

Target

Invitations to visit international laboratories

19 24 27 Agüero – Universidad Técnica Federico Santa María, Valparaiso, Chile, (June/July)

C. Alston – CREST, Paris (March and June)

E. Beh – Second University of Naples, Italy, (February)

J. De Doná – SUPELEC, France (September); Ecole Des Mines de Paris, France (October)

A.J. Fleming – University of Reno, Nevada.

G.C. Goodwin – Universidad Técnica Federico Santa María, Valparaiso, Chile, (February/March); Ericsson, Stockholm, Sweden (August); Imperial College, London (October), Royal Society London, UK (October); Norwegian University of Science and Technology, Trondheim (October).

P. Howley – Taipei Medical University, Taiwan (November); Tunghai University, Taichung, Taiwan (November).

K. Mengersen – CREST, Paris (March); University Kebangsaan, Malaysia.

S.O.R. Moheimani – Hanyang University, Korea (August); Park Systems, Korea (August); Asylum Research, Santa Barbara, (September); Control Group, Oxford University (December)

T. Perez – Universita Politecnica delle Marche, Ancona, Italy (September); Norwegian University of Science and Technology, Trondheim, Norway.

J. Rayner – Ghent University, Belgium (May and October).

F. Tuyl – Erasmus University, The Netherlands (June).

J. Welsh – University of Manchester, UK (June); KTH Stockholm, (July); University of Grenoble, France (July)

15

Number of commentaries about the Centre’s achievements

4 5 1 See Industry Interaction and Selected Outcomes. 6

Additional competitive grant income

$170,000 $2,196,831 $1,528,472 $811,560 ARC Discovery Grants

$538,672 ARC Linkage Grants and partner contributions

$178,240 ARC Future Fellowship

$150,000

RESEARCH FINDINGS AND COMPETITIVENESS (CONT.)


Description2008

Actual2009

Actual2010


2008 – 2010 Target

Postgraduate students recruited

3 8 11 See Postgraduate Research Students. 18

Postgraduate completions

6 6 3 See Postgraduate Research Students. 15

Supervise Honours students

10 7 13 See Undergraduate Research Students. 24

Professional courses run

6 7 7 See Conferences, Courses and Workshops. 3

Participation in professional courses

0 2 1 Perez – Two day course “Tracking and Data Fusion”, NICTA, 11-12 August 2010, Mawson Lakes South Australia.

15

Number and level of undergraduate and high school courses in the priority areas

6 9 3 n ELEC4400 – “Automatic Control” (De Doná)

n ELEC4400 – “Automatic Control” (De Doná) – Singapore

n ELEC4410 – Control System Design and Management (Welsh)

15 under-

graduate courses

RESEARCH TRAINING AND PROFESSIONAL EDUCATION


Description2008

Actual2009

Actual2010


2008 – 2010 Target

Published papers with international co-authors

J: 30 C: 20

J: 33 C: 28

J: 39 C: 33

See Publications Section. 60

International Visitors 26 36 34 See Visitors Section. 30

Collaborative national and international workshops and exchanges

6 1 2 Fleming and Leang – Workshop Presentation at the IEEE Conference on Robotics and Automation, May 3, 2010: “Measurement and Control for High-Speed Sub-Atomic Positioning in Scanning Probe Microscopes”.

Fleming – Co-organiser of invited sessions at ACC 2010: “Nanopositioning and Scanning Probe Systems” and “Advanced Control Methods for Nano-Measurements”

15

Visits to overseas laboratories

20 21 23 J.C. Agüero – Universidad Técnica Federico Santa María, Valparaiso, Chile, (June-July)

C. Alston – CREST, Paris (March and June)

E. Beh – Second University of Naples, Italy, (February)

J. De Doná – SUPELEC, France (September); Ecole Des Mines de Paris, France (October)

A.J. Fleming – University of Reno, Nevada.

G.C. Goodwin – Universidad Técnica Federico Santa María, Valparaiso, Chile, (March); Ericsson, Stockholm, Sweden (August); Lund University, Sweden (October); Imperial College, London, UK, (October); Royal Society, London UK (October) ; Norwegian University of Science and Technology, Trondheim (October).

P. Howley – Taipei Medical University, Taiwan (November); Tunghai University, Taichung, Taiwan (November).

K. Mengersen – CREST, Paris (March); University Kebangsaan, Malaysia.

T. Perez – Universita Politecnica delle Marche, Ancona, Italy (September).

J. Rayner – Ghent University, Belgium (May and October).

F. Tuyl – Erasmus University, The Netherlands (June).

J. Welsh – University of Manchester, UK (June); KTH Stockholm, (July); University of Grenoble, France (July)

45

INTERNATIONAL, NATIONAL AND REGIONAL LINKS AND NETWORKS


Description2008

Actual2009

Actual2010


2008 – 2010 Target

Memberships of national and international professional committees

14 25 17 E. Beh – Vice President (outgoing), NSW Branch, Statistical Society of Australia Inc.

J. Braslavsky – Editorial Board, IEEE Transactions on Automatic Control;

A. Fleming – Associate Editor, Advances in Vibration and Acoustics

G.C. Goodwin – Chair, Sectional Committee, Australian Academy of Science; Advisory Board Member, ARC Centre of Excellence for Autonomous Systems, University of Sydney; Member, Science Advisory Board, Lund University, Sweden; Advisory Board Member, Lund Center for Control of Complex Engineering Systems, University, Sweden.

P. Howley – Section co-chair, Education for the Statistical Society of Australia, Inc.

K. Mengersen – Vice-President, Statistical Society of Australia Inc; Executive Member, International Society for Bayesian Analysis; Regular Member, American Statistical Association; Ordinary Member, International Biometrics Society.

S.O.R. Moheimani – Technical Editor, IEEE/ASME Transactions on Mechatronics; Associate Editor, IEEE Transactions on Control Systems Technology; Vice-Chair, IFAC Technical Committee on Mechatronic Systems; Member, IFAC Awards Committee.

J. Welsh – Associate Editor, IEEE Control Systems Society, Conference Editorial Board.

15

Research projects with international partners

21 25 27 See Research Programs section. 15

INTERNATIONAL, NATIONAL, AND REGIONAL LINKS AND NETWORKS (CONT.)


Description2008

Actual2009

Actual2010


2008 – 2010 Target

Commercialisation activities

2 9 3 n Virtual Laboratories (Goodwin)

n “quantized Zooming in Inner Loop Power Control”. Patent #31996 (Goodwin, Cea and Wigren*).

n “Positioning System and Method” (2010, Published Application PCT: WO 2010/040185 A1 (Fleming)

3

Government, industry and business briefings

7 9 1 Perez – Presentation to industry at “Hunter Net Annual Meeting”, Newcastle Innovation Ltd.

6

Centre associates trained in technology transfer and commercialization

3 1 1 Perez – “Commercialisation Boot Camp”, Newcastle Innovation Ltd.

3

Public awareness programs

0 1 1 Mengersen – Radio Broadcast. 3 national broadcasts

Cash contributions from end-users to the Centre, including research contracts

$215,000 $233,000 $245,000 BHP Billiton; CFW Hamilton Jet (New Zealand); DSTO; Ericsson (Sweden); Matrikon; NTNU

$50,000

In-kind contributions from end-users to the Centre

$354,420 $262,858 $336,583 $100,000

END-USER LINKS


Description2008

Actual2009

Actual2010


2008 – 2010 Target

Annual cash contributions from collaborating organizations

$633,142 $552,371 $562,371 BHP-Billiton $100,000; Matrikon $50,000; The University of Newcastle $300,000; NSW Department of State and Regional Development $80,771; queensland University of Technology $21,600.

$500,000 pa

Annual in-kind contributions from collaborating organizations

$3,402,464 $4,965,483 $6,165,084 $1M pa

New organizations recruited to or involved in the Centre

3 2 4 NSW Ambulance; NSW Department of Agriculture; Tranquility Pools; Chevron.

3

Annual cash contributions from other organizations

$65,000 $93,000 $95,000 DSTO; CFW Hamilton Jet (New Zealand); NTNU (Norway); Ericsson (Sweden)

20,000 pa

Description2008

Actual2009

Actual2010


2008 – 2010 Target

Advisory Board 1 1 1 See Advisory Board. 3

Strategic Plan endorsed by Advisory Board

1 0 0 Accepted on 25 July 2008 1

Management of Centre nodes 6 6 6 Centre Leaders and Deputies Meetings 6 pa

Centre’s Key performance measures

1 1 1 2010 Annual Report 3

Description2008

Actual2009

Actual2010


2008 – 2010 Target

Measures of expansion of Australia’s capability in the priority areas

N/A N/A N/A See Industrial Interaction and Selected Outcomes.

N/A

Case studies of economic, social, cultural

2 0 0 3

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