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DECLARATION OF THESIS / UNDERGRADUATE PROJECT REPORT AND COPYRIGHT
Author’s full name : Mohammed Ameen Abbas AL-Zuraiqi
Date of Birth : 01/01/1989
Title : Modeling and controller design of an industrial pneumatic actuator
system
Academic Session : 2013/2014
I declare that this thesis is classified as:
CONFIDENTIAL (Contains confidential information under the Official Secret Act
1972)*
RESTRICTED (Contains restricted information as specified by the
organization where research was done)*
OPEN ACCESS I agree that my thesis to be published as online open access
(full text)
I acknowledged that Universiti Teknologi Malaysia reserves the right as follows:
1. The thesis is the property of Universiti Teknologi Malaysia
2. The Library of Universiti Teknologi Malaysia has the right to make copies for the
purpose of research only.
3. The Library has the right to make copies of the thesis for academic exchange.
Certified by:
SIGNATURE SIGNATURE OF SUPERVISOR
(NEW IC NO/PASSPORT) NAME OF SUPERVISOR
Date: JULY 2014 Date: JULY 2014
PSZ 19:16 (Pind. 1/07)
NOTES: * If the thesis is CONFIDENTAL or RESTRICTED, please attach with the letter from
the organization with period and reasons for confidentiality or restriction.
UNIVERSITI TEKNOLOGI MALAYSIA
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I declare that I have read this thesis and in my opinion this thesis is sufficient
in terms of scope and quality for the award of the degree of “Bachelor of
Engineering (Electrical - Control and Instrumentation)"
Signature: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Name of Supervisor: Professor Dr Mohd Fua’ad Rahmat.
Date: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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MODELING AND CONTROLLER DESIGN OF AN INDUSTRIAL PNEUMATIC
ACTUATOR SYSTEM
MOHAMMED AMEEN ABBAS AL-ZURAIQI
A project report submitted in partial fulfillment of the requirements for the award of
the degree of Bachelor of Engineering (Electrical - Control and Instrumentation)
Faculty of Electrical Engineering
Universiti Teknologi Malaysia
JULY 2014
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I declare that this thesis entitled “Modeling and controller design of an industrial
pneumatic actuator system” is the result of my own research except as cited in the
references. The thesis has not been accepted for any degree and is not concurrently
submitted in candidature of any other degree.
Signature : ....................................................
Name : MOHAMMED AMEEN ABBAS AL-ZURAIQI
Date : July 2014
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Specially dedicated to my parents
I really miss both of you.
And also to all my family members
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ACKNOWLEDGEMENT
Writing this thesis is a long and difficult journey that requires patients and
dedication. During the entire project, I am glad to receive tons of support from my
family and friends that I really appreciate. The journey would have become harder
and lonely if without their help and encouragement. Second and foremost, I wish to
express my sincere appreciation to my supervisor, Prof Dr Mohd Fua’ad bin Rahmat,
for encouragement, guidance and help.
Besides, I would like to thank my parents and family members for their
support and understanding. Then, I would like to thank my friends who never give
up on me. Thanks to all for continuous support and motivation. I wouldn’t have been
able to complete my project without guidance and consultancy from you all. Thanks
to you all.
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ABSTRACT
A pneumatic actuator is a mechanical device which converts the compressed
air energy into mechanical motion. The motion can be rotary or linear, depending on
the type of actuator. Many industries nowadays use pneumatic actuators in
positioning, clamping, gripping, drilling, and conveying operations in the process of
manufacturing and automation. This is due to the advantages pneumatic actuators
offer over other types of force actuators such as electromechanical and hydraulic
actuators. Although pneumatic actuators have many good attributes, achieving
precise and high-speed control of their systems is a challenge. This difficulty is due
to the high-order, time-variant actuator dynamics, and system nonlinearities like air
compressibility, static and coulomb friction, and pressure supply variations. This
project presents the process of modeling a pneumatic actuator system followed by
designing controllers to improve the system performance. To model the system input
and output data are collected from the pneumatic actuator plant using Simulink file.
System Identification Toolbox is used in order to estimate the mathematical model.
Two model structures are selected which are Auto Regressive Exogenous (ARX) and
Auto Regressive Moving Average Exogenous (ARMAX). Model estimation and
validation are done by analyzing residual correlation and best fit percentage. To
improve the system performance conventional PID and Self tuning fuzzy-PID
controllers are designed. The coefficients of the PID controller are tuned using trial
and error method and Ziegler- Nichols method. The system response of the
pneumatic system is improved significantly when applying conversional PID and
self-tuning Fuzzy-PID controllers. Self-tuning fuzzy-PID controller outperformed
the conversional PID controller with 2.22% overshoot only and faster response.
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ABSTRAK
Penggerak pneumatik adalah satu alat mekanikal yang menukarkan tenaga
udara termampat kepada gerakan mekanikal. Ia boleh menjadi sama ada gerakan
putaran atau gerakan linear, bergantung kepada jenis penggerak yang digunakan.
penggerak pneumatik banyak digunakan dalam industri pada hari ini untuk meletak,
pengapit, menggenggam, penggerudian, dan operasi penghantaran dalam proses
pembuatan dan automasi. Hal ini disebabkan oleh kelebihan penggerak pneumatik
yang menawarkan kelebihan berbanding penggerak jenis lain seperti penggerak
elektromekanik dan hidraulik. Walaupun penggerak pneumatik mempunyai banyak
sifat-sifat yang baik, untuk mencapai kawalan yang tepat dalam kelajuan yang tinggi
didalam sistem mereka merupakan suatu cabaran. Kesukaran ini adalah disebabkan
oleh susunan tinggi, kebergantungan pada masa ciri dinamik penggerak dinamik, dan
sistem yang tidak linear seperti kebolehmampatan udara, geseran statik dan
coulomb, dan variasi bekalan tekanan. Projek ini membentangkan proses pemodelan
sistem penggerak pneumatik diikuti dengan proses mereka bentuk pengawal untuk
meningkatkan prestasi sistem. Untuk membuat model, masukan sistem dan
maklumat keluaran dikumpulkan dari sistem penggerak pneumatik menggunakan
fail Simulink. System Identification Toolbox digunakan untuk menganggarkan
model matematik. Dua struktur model dipilih iaitu Auto Regressevie
Exogenous(ARX) dan Auto Regressive Moving Average Exogenous (ARMAX).
Anggaran Model dan pengesahan dilakukan dengan menganalisis hubungan lebihan
dan peratusan terbaik yang sesuai. Untuk meningkatkan prestasi system, PID
konvensional dan pengawal fuzzy-PID talaan sendiri direka. Pekali pengawal PID
ditala menggunakan kaedah heuristik dan kaedah Ziegler-Nichols. Sambutan sistem
sistem pneumatik bertambah baik apabila pengawal Fuzzy-PID talaan sendiri dan
PID konvensional digunakan. Pengawal Fuzzy-PID talaan sendiri mengatasi
pengawal PID konvensional dengan hanya 2.22% lajakan sahaja dan tindak balas
yang lebih cepat.
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TABLE OF CONTENTS
CHAPTER
TITLE PAGE
ACKNOWLEDGEMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
vii
LIST OF TABLES
x
LIST OF FIGURES
xi
LIST OF ABBREVIATION
xiii
1 INTRODUCTION
1
1.1 Project background 1
1.2 Problem statement 3
1.3 Objectives 3
1.4 Project scope and limitation 3
1.5 Methodology 4
1.6 Report organization
4
2 LITERATURE REVIEW 6
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3 METHODOLOGY
13
3.1 Introduction 13
3.2 Hardware implementation 16
3.2.1 Experimental setup 16
3.2.2 Components list 17
3.2.2.1 Pneumatic actuator 18
3.2.2.2 Electro pneumatic regulator 18
3.2.2.2.1 Working principle of EPR 19
3.2.2.3 Linear position sensor 20
3.2.2.4 Air compressor 21
3.2.2.5 Data acquisition (DAQ) 22
3.2.2.6 Peripheral PCI 23
3.2.3 Hardware configuration 24
3.3 Software Implementation 25
3.3.1 MATLAB System Identification 25
3.3.2 Simulink block diagram 26
3.4 Modeling and controller design approach 27
3.4.1 Model structure 27
3.4.2 Model Estimation and Validation 28
3.4.2.1 Final Prediction Error 28
3.4.2.2 Loss Function 29
3.4.2.3 Best fitting criteria 29
3.4.3 Controller Design 30
3.4.3.1 PID controller Design 30
3.4.3.2 Self-tuning fuzzy PID
31
4 RESULTS AND DISCUSSION
33
4.1 Model estimation 33
4.2 Modeling with multi-sine input 34
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4.3 Modeling with single sine input with 0.01s
....... ... sampling time
36
4.4 Modeling with single sine input with 0.03s
.......... sampling time
40
4.5 Controller design 43
4.5.1 Conventional PID controller design 43
4.5.1 Self tuning Fuzzy-PID controller
................. ...design
47
5 CONCLUSION
53
5.1 Conclusion 53
5.2 Recommendations
55
6 PROJECT MANAGEMENT 56
6.1 Introduction 56
6.2 Project Schedule 56
6.3 Cost Estimation
58
REFERENCES
60
Appendices A&B 62 -68
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LIST OF TABLES
TABLE NO.
TITLE PAGE
3.1 Components list for hardware implementation 17
3.2 Characteristic of Kp, Ki and Kd 31
4.1 Percentage fit result of multi-sine input signal
modeling
35
4.2 Percentage fit result of single sine input signal
modeling (0.01s)
37
4.3 Percentage fit result of single sine input signal
modeling (0.03s)
40
4.4 Ziegler Nichols’ PID controller parameters table 45
4.5 Simulation and experimental results of the
controllers’ performance specifications
52
6.1 Project Gantt chart (Semester 1) 57
6.2 Project Gantt chart (Semester 2) 58
6.3 Main system components prices 59
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LIST OF FIGURES
FIGURE
NO.
TITLE PAGE
3.1 The flow of the main four steps of the project 14
3.2 Flow chart of the project execution 15
3.3 Pneumatic Actuator plant setup 16
3.4 Double acting pneumatic cylinder (300mm) 18
3.5 Physical structure of EPR and the schematic diagram
for EPR
19
3.6 Block diagram for EPR operation 20
3.7 linear position sensor (potentiometer KTC 300mm) 21
3.8 ORIMAS 2HP 24L air compressor 21
3.9 EPRs and position sensor connections to the DAQ 22
3.10 Peripheral Component Interconnect (PCI) card 23
3.11 Wiring connections for physical experiment 24
3.12 System Identification toolbox 25
3.13 Simulink block diagram for data collection 26
3.14 Block diagram for ARX model structure 27
3.15 Block diagram for PID controller structure 30
3.16 Fuzzy logic controller block diagram 32
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3.17 Structure of the self tuning fuzzy PID controller 32
4.1 Multi-sine input signal 34
4.2 Measured and simulated data output comparison 35
4.3 Poles and zeros unit cycle when offset value -0.07 36
4.4 Single sine input signal with sampling time (0.01s) 36
4.5 Measured and simulated data output comparison 37
4.6 Poles and zeros unit cycle when offset value -0.1033 38
4.7 Single sine input signal with sampling time (0.03s) 40
4.8 Measured and simulated data output comparison with
sampling time (0.03s)
41
4.9 Poles and zeros unit cycle when offset value -0.18 42
4.10 Conventional PID controller Simulink block diagram 44
4.11 Simulation result of PID controller step reponse 45
4.12 Experimental step response result of PID controller 46
4.13 Fuzzy inference block diagram 47
4.14 Membership function of e(t) 48
4.15 Membership function of de(t) 48
4.16 Membership functions of K’p, K’I, K’d 48
4.17 Simulink block diagram of fuzzy PID regulator 50
4.18 Simulink block diagram of the system controllers 50
4.19 Simulation of self-tuning fuzzy PID controller step
response result
51
4.20 Simulation of self-tuning fuzzy PID controller step
response result
51
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LIST OF ABBREVIATION
ARX - Auto-Regressive Exogenous
ARMAX - Auto-Regressive Moving Average Exogenous
BJ - Box-Jenkin
OE - Box-Jenkin
PID - Proportional –Integral-Derivative
EPR - Electro pneumatic regulator
FPE - Final Prediction Error
V - Voltage
DAQ - Data Acquisition
NI - National Instrument
LVDT - Linear Variable Differential Transformer
I/O - Input and Output
% - Percentage
SISO - Single Input single Output
MIMO - Multi Input Multi Output
GUI - Graphic User Interface
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CHAPTER 1
INTRODUCTION
1.1 Project background
An actuator is a mechanical device that is used for controlling or moving
mechanism. Particularly, it is a device that transforms an input signal represented in the
form of fluid, air, or electrical signal into a useful motion and it’s mostly used in
applications and equipments which require circular or linear motion. The usual
mechanisms of actuators are used to apply motion, clamp an object, or prevent motion.
They are divided into four board categories: hydraulic actuators, pneumatic actuators,
eclectic actuators, and mechanical actuators. In this project, a pneumatic actuator is used
to achieve the required objectives of the study.
A pneumatic actuator converts the compressed air energy into a useful
mechanical motion. The motion can be linear or rotary depending on the actuator type.
Many industries nowadays use pneumatic actuator as positioning, clamping, gripping,
drilling, and conveying operations in the process of manufacturing and automation. This
is due to the advantages of pneumatic actuators that offer over other type of force
actuator such as electromechanical and hydraulic actuator. Pneumatic actuators have
many attributes which make them very attractive to be used in the industry of robotics
and automation. Some of their attributes are: simple and low cost technology, good
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power density, fast acting with high acceleration, safe and clean technology, self cooling
properties, easy maintenance and handling, and compressed air is available in almost all
industry plants.
Although pneumatic actuators have many good attributes, achieving precise and
high-speed position control of their systems is a challenge. This difficulty is due to the
actuator’s static and coulomb friction, the high-order, time-variant actuator dynamics
and system nonlinearities like air compressibility, and pressure supply variations. Other
bad characteristics of pneumatic systems are: dead band and dead time [1].
Many researchers investigated pneumatic actuators characteristics to enable
getting the advantages of pneumatic actuators’ attributes and to enable compensating
their disadvantage like position control difficulty. several approaches in control and
modeling of pneumatic actuators have been proposed by many researches around the
world. Many advanced control algorithm were proposed like modified PID, fuzzy logic,
neural networks, adaptive controllers, and genetic algorithms.
The range and operating performance of pneumatic actuators has expanded
considerably in recent years, keeping consistent growth in the field of robotics and
automation systems. An actuator or a combination of actuators has the possibility to
meet the needs of almost every application. Performance and efficiencies can be
improved and overall costs be decreased when applying pneumatic actuation in the right
application. The applications can be: end effectors integration, wash-down actuator,
multiple position control, and extremely accurate and high speed control.
In designing robots’ end effectors for some applications like mechanical gripper
or vacuum suction cups, pneumatic actuation can provide better yet cheaper solutions
than other actuation methods. Suction cups are ideal for handling work-pieces of
different shapes, sizes and surface finishes. This technique can be applied when high
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positioning accuracy is not required. An application of Suction cups is picking and
placing delicate items, like glass and fresh produce [1].
1.2 Problem statement
Precise and high-speed position control of pneumatic systems is a challenge due
to the high-order, time-variant actuator dynamics, and system nonlinearities like air
compressibility, static and coulomb friction, and pressure supply variations.
1.3 Objectives
The objectives of the project are:
1. To model the pneumatic actuator system using system identification and
estimation approach.
2. To design PID and self-tuning fuzzy-PID controllers to improve the
pneumatic actuator performance.
3. To implement and validate the model and the controller design on the
pneumatic actuator experimental set-up.
1.4 Project scope and limitations
In this project, a pneumatic actuator plant is used to achieve the above objectives.
This project does not consider other types of actuators like hydraulic or
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electromechanical actuators. The pneumatic actuator used is a double acting cylinder.
System Identification approach is use as a tool to estimate the mathematical model of the
pneumatic actuator plant. Two modeling structures: Auto-Regressive Exogenous (ARX)
and Auto-Regressive Moving Average (ARMAX) are used to estimate the mathematical
model. There is no involvement of modeling using theoretical and physical formulas and
laws. The controller design development is based on conventional PID controller and
self tuning fuzzy PID controller. The major limitation of the project is that the pneumatic
experimental set-up does not have a load holder. As a result, the adaptive performance
of the controllers cannot be tested.
1.5 Methodology
The pneumatic actuator experimental set-up was used as a tool to achieve the
above three objectives. The pneumatic actuator system was modeled using system
identification toolbox. Input and output data were collected from the experimental
pneumatic actuator. Two model structures were selected which are Auto Regressive
Exogenous and Auto Regressive Moving Average Exogenous Model estimation and
validation were done by analyzing residual correlation and best fit percentage. To
improve the system performance conventional PID and self tuning fuzzy-PID controllers
are designed by using Matlab software. The coefficients of the PID controller are tuned
using trial and error method and Ziegler- Nichols method. Finally, the model and
controllers were applied and validated on the experimental set-up.
1.6 Report organization
This report consists of six chapters: introduction, literature review, methodology,
results and discussion, conclusion, and project management. Introduction chapter
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presents the project proposal. Literature review chapter summaries the work of previous
researchers who work in a similar projects to this project. In the third chapter, the
hardware and software implantations are explained as well as the steps taken to make
this project a great success. In the following chapter, the results obtained in this project
were introduced and analyzed. The conclusion chapter summarizes the important points
and findings of this project and gives some recommendations for future works. The last
chapter shows the Gantt chart and cost estimation of the project.
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CHAPTER 2
LITERATURE REVIEW
Although Pneumatic actuators have many good attributes which make them very
attractive in the industry, Precise and high-speed position control of pneumatic systems
is a challenge. As a result, several approaches in control and modeling of pneumatic
actuators have been proposed by many researches around the world. Many advanced
control algorithm were proposed like modified PID, fuzzy logic, neural networks,
adaptive controllers, and genetic algorithms. This chapter introduced various theoretical
and experimental modeling approaches as well as control strategies applied to pneumatic
actuators.
A review of pneumatic actuators’ modeling and controller design technique was
presented in the literature surveyed. The author presented a detailed a review of
literature that related of the pneumatic actuator systems. He reviewed particularly the
innovations in different modeling and control strategies implemented to pneumatic
systems. He reviewed controlling and simulation techniques proposed for different
applications of pneumatic systems. The author also concentrated on the analysis,
investigation, performance, practical constraints, nonlinearities, uncertainties and the
new applications of the pneumatic actuators [1].
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A practical control technique applied on pneumatic actuator system was
introduced in the literature. The stability of the pneumatic cylinder system was improved
by developing control strategy using acceleration feedback instead of pressure difference
feedback. The problems of time delay and dead zone caused by the air compressibility
and friction were addressed by introducing time-delay minimization and optimized null
offset compensation [2].
In the literature, a system identification strategy which was implemented to get a
linear time-invariant model was present. The author discussed the effects of different
parameters on the identification results. The parameters handled were: sampling time,
model order, amplitude of test signal, shift-register number of the PRBS signal, and
pressure supply. Finally a PID controller was designed according to the ITAE optimal
control criterion [3].
Pressure observer-controller designed for pneumatic systems was investigated in
the literature. The chamber pressure variables in pneumatic cylinder actuator were
estimated by designing suitable observers. A continuous gain observer was proposed in
which the pressure on one side is measured and the other is estimated. This is to
compensate for the cylinder natural dynamics where the pressure cannot be observed at
the two champers simultaneously. Finally a sliding-mode controller is proposed to
observer both pressures using numerically estimated acceleration [4].
Nonlinear modeling and control strategy of pneumatic systems was proposed in
the literature. Combinations of mechanistic and empirical methods were used to develop
a nonlinear model of the system. A more accurate solution than prior approaches was
produced by using novel bi-polynomial functions to model the valve flow rates. The
researcher used the back-stepping methodology to design a novel multiple-input single-
output nonlinear position control law. The author also investigated effects of friction
modeling error and valve modeling error to obtain a more stable system [5].
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In the literature, a technique for identification of pneumatic cylinder friction
parameters using genetic algorithms was explained. The author studied different
evaluation functions. He found two evaluation functions to have the expected rate of
convergence precision. The algorithm is initially developed and tested using the
benchmark data generated by simulations. Those algorithms are used for parameters
identification using the data obtained from the real system measurement [6].
In the literature, an identification approach of a pneumatic actuator using non-
linear black-box model was proposed. The author presented an accurate non-linear back-
box model (NBBM) for identifying the dynamic behavior of pneumatic actuators.
Generation of an effective solution for designing a position controller becomes feasible
after finding the optimized NBBM of the pneumatic actuator. The author designed a
multi-player perceptron neural network (MLPNN), whose parameters were optimized by
using the Lervenberg-Marquardt Back Propagation (LMBP) algorithm [7].
Modeling and controller design of a pneumatic system inverted pendulum was
done in the literature. The author derived a linearized model based on a nonlinear model
of the overall pendulum system, which also includes notable friction effects. The
linearized model was used to design State feedback controller based on Linear Quadratic
and Linear Quadratic Gaussian optimization procedures was designed. The linear state
feedback controllers are augmented by a compensator of nonlinear friction effects whose
design is based on the results of experimental identification of an appropriate static
friction model [8].
Identification of a nonlinear pneumatic servo system using modular neural
networks was proposed in the literature. Due to the difficulty of modeling and control of
pneumatic actuators using traditional method, the author thinks neural networks are
good alternative. A modular neural network for the identification of a pneumatic servo
system was proposed. This approach is based on the partitioning of static characteristic
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of the pneumatic system. The neural modules are implemented with multilayer neural
networks [9].
A servo controlled pneumatic system for small application to an adaptive gripper
was suggested in the literature. The author presented a new servo controlled pneumatic
actuator that is composed of a low friction cylinder using metallic bellows and a PWM
pneumatic servomechanism. The author established an equivalent mass flow rate model
to consider the opening and closing delays characters. The model obtained is then used
to design a nonlinear control method to compensate the nonlinear and dissymmetric
problems of the pneumatic actuator [10].
In the literature, a robust identification technique for pneumatic systems in real
situations was proposed. He considered a new mathematical model for pneumatic
system. The change of parameters of the model is described by random walk. It is
assumed that the cylinder is described by means of the output error model, where the
measurement noise is non-Gaussian. Masreliez-Martin filter was the natural frame for
identification. Heuristic modifications of the mentioned filter which considerably
increase its practical values were performed [11].
In the literature, a robust adaptive fuzzy control of uncertain nonlinear time-
delay systems with an unknown dead-zone was suggested. The author presented a
robust adaptive fuzzy control strategy without considering the dead-zone inverse. Fuzzy
logic system based on some adaptive laws was used to estimate the unknown nonlinear
functions of the system. The proposed robust adaptive fuzzy control scheme can
guarantee the robust stability of the whole closed-loop nonlinear time-delay system with
an unknown dead-zone. At the end, the author provided examples and simulation result
to show the efficiency of the proposed control strategy [12].
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An experimental comparison between several pneumatic position control
methods was done in the literature. The author presented an experimental comparison
between six different techniques to control the position of a pneumatic actuator. Six
different reference signals have been tested on each control technique. The methods
considered were: PID, Fuzzy, and PID with pressure feedback, Fuzzy with pressure
feedback, E) Sliding mode, and F) Neuro-Fuzzy control. In the last method, proposed by
the authors, the differential pressure sensor has been replaced by a neural network based
estimator [13].
In the literature, an intelligent control strategy for state dependent nonlinear
pneumatic actuator and its application to pneumatic actuators was proposed. The
researcher proposed an intelligent controller based on predictive fuzzy control using a
control rule and a forward model. The current state and the input-output characteristics
of the actuator were used to design the model. A pneumatic servo system was used to
confirm the effectiveness of the proposed intelligent controller experimentally [14].
In the literature, an identification and self-tuning control of electro-pneumatic
actuator system with control valve was introduced. To compensate for the system time
varying parameters, the author proposed a self-tuning controller based on the pole-
assignment controller. An online Recursive Least Squares algorithm updates the
parameter estimation at every sample interval. The pole-assignment control parameter is
then updated accordingly to the change of system parameters. Self-tuning controller
achieved a very good performance with almost Zero steady state error and less than 1%
overshoot [15].
Non-linear modeling and cascade control of an industrial pneumatic actuator
system was introduced in the literature. Physical fundamentals of mass flow rate,
motion equations and pressure dynamics were used to derive the nonlinear model of the
system. Cascade controller based on P and PID controller was designed using
SIMULINK simulation where the parameters were obtained through PID with
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optimization toolbox. Step and sinusoidal responses results showed that cascade
controller method consistently outperformed the classical PID method [16].
In the literature, an identification and non-linear control strategy for industrial
pneumatic actuator was presented. The trajectory tracking of a pneumatic positioning
system was controlled by a combination of nonlinear gain and proportional integral
derivative (NPID). An auto-regressive moving average with exogenous (ARMAX)
model was used as a model structure of the system. The results showed that nonlinear
gain and proportional integral derivative (NPID) controller is better than conventional
PID controller in terms of robust performance as well as show an improvement in its
accuracy [17].
Modeling and controller design of pneumatic actuator was presented in the
literature. The Modeling structure used was Auto-Regressive Exogenous model
structure. Different control algorithms like PID and LQR were implemented for
controller design. The results obtained in the experiment confirmed that the output
signals which with the controller are almost the same for both simulation and
experimental modes [18].
An Application of optimization technique for PID controller tuning in position
tracking of pneumatic actuator system was proposed in the literature. The optimal PID
control parameters were obtained by using two optimization techniques of Particle
Swarm Optimization and Firefly Algorithm. To represent the model of the system,
system identification with ARX model structure is developed. The results are
determined by analysis the step response characteristic of the system. The performances
of PID controller with PSO optimized parameters achieved a good position tracking of
the pneumatic actuator system [19].
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Application of self-tuning fuzzy PID controller on industrial hydraulic actuator
using system identification approached was introduced in the literature. The controllers
were designed based on the model obtained using system identification toolbox. To
tuning the PID parameters, the author used fuzzy logic. The fuzzy rules were selected
appropriately to tune the PID controller. The performance of the hydraulic system has
improved significantly compared to performance conventional PID controller has
achieved [20].
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CHAPTER 3
METHODOLOGY
3.1 Introduction
To achieve the three objectives effectively, the project must go through four
main steps as shown in Figure 3.1: performing experimental data collection to obtain the
input and output signals for modeling, obtaining the model using system Identification
tool box, designing controllers, and validating the model and the controller design on
real time.
The data collection process was performed by using an experimental pneumatic
actuator plant setup shown in Figure 3.3. The data was taken by applying a sinusoidal
input to the pneumatic plant and observing the output of the plant. The model which
meets the selection criteria was obtained by using system Identification tool box and
applying two model structures: ARX and ARMAX. After that, controller design was
taken place by using MATLAB software. Two controllers were designed: conventional
PID controller and self tuning fuzzy PID controller. Finally, the model and controllers
were implemented and validated in real time.
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Figure 3.1: The flow of the main four steps of the project
This chapter discusses the hardware and software implantation of the project and
it explains the modeling and controller design approaches. It explains about all the
hardware components needed in this project and the software tools used in the modeling
and controller design process. It also discusses the modeling technique and approach
used in this project as well as the controllers implemented in this project.
Figure 3.2 shows the general flow of the project execution from the very
beginning until the very end. It illustrates the main tasks that must be done in a particular
sequence. It indicates the main tasks that cannot be done unless another task has been
finished previously.
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Figure 3.2: Flow chart of the project execution
Start
Searching for Background Information
Identifying project’s problem statement, objectives, and
scope
Identifying project objectives and problem statement
Select a project
Literature review on previous works
Select and a pneumatic plant to work on
Collecting data from the process plant
Estimate and validate the system's model
Obtained Model is
appropriate?
Yes
No
Design controllers based on self-tuning controller and self-
adaptation fuzzy controller
Simulate the designed controllers
Implement and validate the designed controllers on real time
Thesis writing
End
Yes
Design specifications
are met?
No
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3.2 Hardware implementation
In the section the pneumatic actuator experimental set-up is introduced. It also
lists down all the hardware components used in the experimental set-up. The
components are discussed for functionality and specification.
3.2.1 Experimental set-up
The experimental setup consists mainly of a double acting pneumatic cylinder.
The experimental setup is shown if Figure 3.3. The pneumatic actuator is energized by
compressed air from the air compressor. The flow of the compressed air is controlled by
to electro-pneumatic regulators (EPR). The EPRs control the flow of the compressed air
depending on an eclectic signal sent from the system’s computer as an input. The
position of the pneumatic cylinder rod is sensed by linear position sensor called
potentiometer.
Figure 3.3: Pneumatic Actuator plant setup
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The potentiometer data are sent to the system computer as an output. The input to
the EPRs is sent through data acquisition card (DAQ) and the output is red also by the
DAQ. The DAQ data are interfaced by Peripheral Component Interconnect (PCI) card
with MATLAB software to collect data from experimental hardware. This card capable
to acquire, analyze and process present data without programming. MATLAB software
is used to collect data for modeling. The controller design is also implemented by using
MATLAB. The model and controller design is validated using the plant setup shown
below.
3.2.2 Components List
Table 3.1 list down the hardware components used in the experimental set-up
shown in Figure 3.3
Table 3.1: Components list for hardware implementation.
No. Items Quantity Specifications
1 Pneumatic cylinder 1 RC2A12300A
Stroke length: 300mm
Pressure: 1.0Mpa 2 Electro pneumatic
regulator(EPR)
2 SMC ITV1000
3 Linear Displacement sensor 1 KTC 300mm
4 Peripheral Component Interconnect
(PCI) card 1 NI SCB-68
5 Compressor 1 ORIMAS 2HP 24L
6 NI DAQ card 1 NI CB-68
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3.2.2.1 Pneumatic Actuator
Pneumatic actuator is class of devices and mechanism that convert pneumatic
power into useful mechanical work or motion. The mechanical motion produced can be
rotary or linear depending on the actuator type used. Figure 3.4 shows a pneumatic
actuator with 300mm stroke has been used in the experimental setup. A double acting
cylinder rod which can be extended and retracted by pressurized air that acts on either
side of the rod. A directional valve controls the flow direction of the pressurized air
which goes in and out the cylinder. The valve used in this project is an electro
pneumatic regulator. The advantage of using a double acting cylinder is the ability of
enabling pressurized air to create a pushing and pulling force on the cylinder.
Figure 3.4: Double acting pneumatic cylinder (300mm)
3.2.2.2 Electro pneumatic regulator (EPR)
Electro pneumatic regulator is used to control pressure intake in the actuator
champers. It acts as a pressure controller at discrete level. The pressure can be changed
by varying the input voltage. The pressure is proportional to the electrical input applied.
Figure 3.5(a) and (b) show the electro pneumatic regulator physical structure and
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schematic diagram for ITV1000 series. Appendix A shows the standard specifications of
the electro-pneumatic regulators.
Figure 3.5: (a) Physical structure of EPR, (b) schematic diagram for EPR
3.2.2.2.1 Working Principle of EPR
The air supply solenoid valve (1) switch ON when the input signal rises, and the
exhaust solenoid valve (2) switch OFF. As a result, the air supply goes through solenoid
valve (1) and is applied to the pilot chamber (3). The pressure in the pilot chamber (3)
rises and applies on the upper surface of the diaphragm (4). Therefore, the air supply
valve (5) linked to the diaphragm (4) opens, and a specific amount of the air supply
becomes output pressure. This pressure feeds back to the control circuit (8) via the
pressure sensor (7). The operation continues until the air pressure supply is proportional
to the electrical input signal.
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Figure 3.6: Block diagram for EPR operation
Each voltage given will present the pressure that need to be released. In this
project, two EPRs were used because the system used a double acting actuator. EPR
operation can be representing in block diagram as shown in Figure 3.6
3.2.2.3 Linear Position sensor
To read the position of the cylinder’s rod linear position sensor is used. Figure
3.7 shows a linear position sensor is resistive type sensor called a potentiometer. In this
experimental setup, KTC-300 linear position transducer selected. KTC-300 is a
potentiometer sensor with linear resistance that tracks for precise measurement and
control of mechanical movement. The sensor is connected as a voltage divider and gives
a clean and noise-free DC-output signal. The stroke length is 300mm. Input voltage
range is between 12V to 40V. KTC-300 position sensor shown in Figure 3.7 and its
standard specifications and features are shown in Appendix B.
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Potentiometer variable resistance can be used for linear displacement
measurements. Conductive wiper slider across a fixed resistive element is used to
connect the sensor as a voltage divider. A voltage source is needed to be applied across
the resistance. As a result, a voltage divider circuit is created to measure the output
voltage which is proportional to the linear displacement. The type of potentiometer used
in this project is a linear potentiometer.
Figure 3.7: Linear position sensor (potentiometer KTC 300mm)
3.2.2.4 Air Compressor
The air compressor shown in Figure 3.8 is the mian powr source in the system to
drive the pneumatic actuator.
Figure 3.8: ORIMAS 2HP 24L air compressor
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Air supply is generated by ORIMAS air compressor. It is two horse powers
(2HP) with 24 liter tank. The weight is 30 kg and the dimension in mm is 600 X290 X
645. Figure 3.8 shows the air compressor used. The compressor will stop pumping the
air into the tank when the pressure reaches 150 psi.
3.2.2.5 Data acquisition (DAQ card)
Since the pneumatic actuator system is analog and the computer is digital system,
using a tool to do the analog and digital conversions is necessity. As a result, the data
acquisition card shown in Figure 3.6 is used.
Figure 3.9: EPRs and position sensor connections to the DAQ
This card is used to get the reading of position sensor and to send the input signal
to the EPRs. DAQ is interfaced with MATLAB software in order to get sampling data
from the experiment. The NI SCB-68 is a shielded input and output connector block for
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interfacing input and output signals to plug-in data acquisition devices with 68-pin
connectors. It is compatible with single- and dual-connector NI X Series and M Series
devices with 68-pin connectors. The pin connections to the plant input and output is
shown in Figure 3.9
3.2.2.6 Peripheral Component Interconnect (PCI) card for NI SCB-68
The National Instrument PCI-6221 is shown in Figure 3.10. This PCI card is
interfaced with MATLAB software to collect data from experimental hardware. This
card capable to acquire, analyze and process present data without programming.
Figure 3.10: Peripheral Component Interconnect (PCI) card for NI SCB-68
The Peripheral Component Interconnect (PCI) bus is one of the most commonly
used internal computer buses. With a shared bandwidth of 132 MB/s, PCI offers high-
speed data streaming and deterministic data transfer for single-point control applications.
The data acquisition hardware has multifunction I/O boards up to 10 MS/s, up to 80
channels, and up to 18-bit resolution.
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3.2.3 Hardware Configuration
The following figure shows the schematic diagram of the physical connections
between electro-pneumatic regulators (ERPs) and the data acquisition card (DAQ).
Figure 3.11 Wiring connections for physical experiment
The connections of EPRs and position sensor to the DAQ are shown in Figure
3.11. This connection is also used to test the position sensor and the EPRs. The
potentiometer is sourced by positive source of +12V and -12V. The voltage of the
potentiometer is proportional to position of the rod (slider). EPR1 and EPR2 are
powered 12-15 VDC source; the voltage of the applied input signal is between 0-5V.
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3.3 Software Implementation
In this section, the software programs used are explained. The main software
used is Matlab software. Three main Matlab toolboxes are used in this project which are:
system identification, Simulink, and fuzzy logic.
3.3.1 MATLAB System Identification Toolbox
System Identification (SI) toolbox as shown in Figure 3.12 is a graphical user
interface used for estimating and analyzing linear and non linear models in the System
Identification. System Identification Toolbox™ software lets you estimate linear and
nonlinear mathematical models of dynamic systems from measured data. Use the
resulting models for analyzing system dynamics, simulating the output of a system for a
given input, predicting future outputs based on previous observations of inputs and
outputs, or for control design. SI is particularly helpful for modeling systems that you
cannot be modeled easily theoretical formulas and laws like engine subsystems, thermo
fluid processes, and electromechanical systems.
Figure 3.12: System Identification toolbox
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The toolbox shown in Figure 3.12 uses input and output data to estimate the
mathematical model. The input and output data can be in time domain or frequency
domain. It can be used to estimate a variety of model structures. It also enables the user
to investigate the model using the model frequency response, time response, residues,
zeros and poles, and so on.
3.3.2 Simulink Block Diagram for Data collection
Figure 3.13 shows the Simulink file which was used for data collection. This file
sends input signals to the two EPRs to control the pressure in the cylinder champers. The
file also takes the output data from the position sensor.
Figure 3.13: Simulink block diagram for data collection
In the figure above, there are two analog outputs representing the systems two
EPRs. There is also one analog input representing the position sensor. The input signal
to the system is a sine wave which be changed to be a multi sine signal or a unit step
signal depending on the need of the user.
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3.4 Modeling and controller design approach
System identification approach is used with ARX and ARMAX as the model
structures. The model criterion is based on the Akaike’s Final Prediction error (FPE),
loss function, and best fitting criteria. The controller design is based on conventional
PID controller and self tuning fuzzy PID controller.
3.4.1 Model structure
Data is imported into identification toolbox for various data processing, model
estimation, and model analysis. The input and output data can be in time domain or
frequency domain. Time domain plot is chosen for the pneumatic actuator analysis
because the output signal is time variant. After that, model structure is selected for
model estimating purpose. The model structure selection is based on prior knowledge or
understanding of the system being modeled. In this project, ARX is the main model
structured used due to its simplicity and efficiency. Compared to ARMAX, Box-Junkin
(BJ) and Output Error (OE) model structures, it is able to solve the linear regression
equation in analytic form. Besides, its solution fulfils the global minimum of loss
function. Figure 3.14 shows the block diagram for ARX model structure. After that,
model order is determined based on experimental analysis or pervious knowledge.
Figure 3.14: Block diagram for ARX model structure
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The ARX model is the simplest model incorporating the stimulus signal.
However, the ARX model captures some stochastic dynamics as a part of the system
dynamics.
3.4.2 Model Estimation and Validation
System Identification toolbox offers parametric and non paramedic approaches to
perform model estimation. To use system identification toolbox, input and output data
must be imported. After that, half of the data is used for model estimation and the other
half is used for model validation. Model estimation describes system behavior through
mathematical models which include distribution function, statistical probability,
parametric dynamic models and data-based Simulink model. Once the estimation
process completed, the selected model is validated in order to determine the
reproducibility of system behavior within acceptable bounds. Model estimation and
validation are iterated to find the simplest model that best captures the system dynamics.
Model estimation and validation can be done by observing the final prediction error
(FPE), loss function and best fit percentage of system dynamics.
3.4.2.1 Akaike’s Final Prediction Error (FPE)
Akaike’s Final Prediction error (FPE) provides a measure to model quality by
simulating the situation where the model is tested on a different data set. If the same data
set is used for model validation and estimation, the fit always improve as model order
increased. According to Akaike’s terminology, the most precise model has the minimum
FPE.
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3.4.2.2 Loss Function
Loss function is considered as a function that maps an event into real number
intuitively representing some “cost” related to the event. It is typically used form
parameter estimation as well as the difference between estimated and actual values for
the instance of data. The value of the loss function itself is a random quantity which
associated to the difference between measured and true values. Model estimation and
validation are based in the expected value of the loss function, basically model with
lowest average loss is chosen.
3.4.2.3 Best fitting criteria
Best fitting criteria is defined as similarity between measured and true value in
the unit of percentage. Typically, it indicates the performance and the behavior of
system dynamic in responding the change of input signal. Certainly, model structure
with the highest percentage will be chosen. Equation (3.1) describes the calculation in
obtaining the best fit percentage.
(3.1)
Where,
y= true value
= approximate value
= mean value
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3.4.3 Controller Design
This section explains the two controller design approaches utilized in this
project. The control strategies are conventional PID controller and self tuning fuzzy PID
controller.
3.4.3.1 PID controller Design
PID controller is commonly used in industrial control as generic control loop
feedback mechanism. System error is calculated by observing and comparing the
difference between the measured control variable and desired set point. PID controller
can be adjusted to tune coefficient of proportional, integral and derivative gain with the
purpose to compensate the feedback error [20].
Figure 3.15 shows the basic block diagram of PID controller. It is be summation
of proportional, integral and derivative gain constant before the controller signal enter
the process plant.
Figure 3.15: Block diagram for PID controller structure
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It’s mathematically represented by the following equation:
Y(t)= Kp [e(t) + Td
]
Y(t)= Kp [e(t) + Kd
] (3.2)
where: Ki = Kp / Ti ; and Kd = Kp . Td
Table 3.2 demonstrates and summarizes the behaviors of proportional, integral and
derivative action individually.
Table 3.2 Characteristic of Kp, Ki and Kd
Response Rise time Overshoot Settling time S.S Error
Kp Decrease Increase NT* Decrease
Ki Decrease Increase Increase Eliminate
Kd NT Decrease Decrease NT*
*NT: No defined trend. Minor change
The performance specifications of the systems such as rise time, overshoot,
settling time and error steady state can be improved by tuning value of parameters Kp, Ki
and Kd of the PID controller, because each component has its own special purposes.
3.4.3.2 Self-tuning fuzzy PID controller design
Fuzzy logic controller as shown in Figure 3.17 consists of main four parts
fuzzification, rule base, inference engine and defuzzification.
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Figure 3.17: Fuzzy logic controller block diagram
Self-tuning fuzzy PID controller means that the three parameters Kp, Ki, and Kd
of PID controller are tuned by using fuzzy tuner. The coefficients of the conventional
PID controller are not often properly tuned for the nonlinear plant with unpredictable
parameter variations. Hence, it is necessary to automatically tune the PID parameters.
The structure of the self-tuning fuzzy PID controller is shown in Figure 3.17 where e(t)
is the error between desired position set point and the output, de(t) is the derivation of
error. The PID parameters are tuned by using fuzzy inference, which provide a nonlinear
mapping from the error and derivation of error to PID parameters [20].
Figure 3.17: structure of the self tuning fuzzy PID controller
Figure 3.17 shows a closed loop with an electro-pneumatic plant. The plant
receives it manipulated variable from the PID controller. The PID parameters are tuned
using the fuzzy inference regulator. The regulator takes the error signal and its
derivative as its inputs.
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CHAPTER 4
RESULTS AND DISCUSSION
4.1 Model estimation
Model estimation of the pneumatic system was achieved by general method of
system identification in which input and output data were used. The pneumatic system
was operated to obtain good data for the modeling process. Figure 3.13 shows the
Simulink file used to collect the input and output data. In the data collection process,
sine and multi-sine input singles were tried with different sampling time and different
operation time. The sampling times used were 0.01s, 0.03s, and 0.05s. The system was
operated for 50s, 80s, or 100s to get a good data that represent the system dynamic
behavior well. ARX and ARMX model structures were selected to produce the model.
Model Validation was done by comparing the estimated model input and the real
experimental output.
This chapter discusses the results obtained for multi sine modeling and single
sine modeling. After that, conventional PID controller and self-tuning Fuzzy-PID
controller results were discussed and compared.
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4.2 Modeling with multi-sine input
The multi-sine input signal in equation (4.1) is applied to the system shown in
Figure 3.13 and the output signal was observed. Three different sampling time period
were used: 0.01s, 0.03s, and 0.05s with operation time of 80s. The input and output
signals were then used for system estimation and validation process. Figure 4.1 shows
the multi-sine input for model identification.
U (t) = 0.8(0.5sin (2*pi*0.5t) +1.5sin (2*pi*0.8t) + 0.8sin (2*pi*1.6t) (4.1)
Figure 4.1: Multi-sine input signal
Figure 4.2 shows the comparison of the estimated data and the validation data.
Table 4.1 shows some of the percentage fit results obtained when applying multi-sine
input to the system. The model structure used is ARX structure. Three sampling times
were used: 0.01s, 0.03s, and 0.05s. By observing the data obtained in Table 4.1, it’s clear
that the percentage fit is very low and don’t represent the system well. As a result, they
are not adequate for controller design application. Besides the low percentage fit results
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obtained, the models were not stable due to the existence of one pole outside the unit
cycle. Figure 4.3 shows the poles and zero graph at -0.07 offset value, model structure
ARX331, and the sampling value is 0.05s. It is clear that the system is not stable.
Table 4.1: percentage fit result of multi-sine input signal modeling
Offset Best fit (%)
ARX331(0.01s) ARX331(0.03s) ARX331(0.05s)
-0.05 78.66 75.54 78.22
-0.07 69.29 72.46 78.48
-0.1 64.43 66.02 73.65
-0.11 56.27 62.58 74.51
-0.12 61.31 65.81 66.98
-0.13 60.63 59.70 69.21
-0.14 66.73 60.33 65.23
-0.15 58.76 62.02 6.10
-0.16 58.45 59.09 55.71
Figure 4.2: Measured and simulated data output comparison
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Figure 4.3: Poles and zeros unit cycle when offset value -0.07
4.3 Modeling with single sine input with 0.01s sampling time
The single sine input signal in equation (4.2) is applied to the system shown in
Figure 3.13 and the output signal was observed. The sampling time period is 0.01s and
operation time is 50s. The input and output signals were used for system estimation and
validation process. Figure 4.4 shows the single sine input for model identification.
U (t) = 3sin (2*pi*2 t) (4.2)
Figure 4.4: Single sine input signal with sampling time (0.01s)
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Table 4.2: Percentage fit result of single sine input signal modeling (0.01s)
Offset
Best fit (%)
ARX331 ARX441 AMX2221 AMX3331
-0.10 86.72 86.61 86.61 87.02
-0.11 85.88 85.90 85.86 85.97
-0.1033 91.25 91.29 91.25 91.25
-0.115 85.50 84.47 85.49 85.49
-0.12 83.83 83.79 83.82 83.83
-0.13 84.55 84.23 84.20 84.22
-0.14 81.58 81.51 81.38 81.56
-0.15 77.67 77.70 77.69 77.69
-0.16 81.88 81.89 81.89 81.90
-0.17 86.70 86.73 87.91 87.91
-0.18 85.74 85.78 85.95 85.97
Figure 4.5: Measured and simulated data output comparison
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Table 4.2 shows the percentage fit results of the single sine input modeling with
a sampling time of 0.01s. Four model structures were used which are: ARX331,
ARX441, ARM2221, and ARM3331. The offset value was changed from -0.1 to -0.185.
By observing the data in the table, it’s clear that the ARM structures percentage fit is
better than ARX structures because ARM structure includes the stochastic part in its
model. Table 4.2 suggests that when the offset value is increased the general percentage
fit value decreases.
Figure 4.5 shows the measured and simulated data comparison when the offset
value is -0.1033. Table 4.2 shows that the best percentage fit was achieved when the
offset value is equal to -0.1033. Since ARX331 and ARX441 have almost the same
percentage fit, ARX331 was chosen for further analysis because it has a lower order.
Figure 4.6: Poles and zeros unit cycle when offset value -0.1033 (ARX331)
Since ARX331 has a good percentage fit of 91.25%, further analysis is done to
ensure the model adequacy for controller design. The model loss function value equals
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to 0.00429832 and final prediction error (FPE) equals to 0.00431911. Percentage fit, loss
function, and FPE suggest that this model is good enough to be used for controller
design.
Figure 4.6 shows the zeros and poles positions of the ARX331 model in the unit
cycle. This figure shows clearly that all the poles are in the unit cycle. As a result, the
system is stable which also support the adequacy of the model to be used for controller
design.
(4.3)
Equation 4.3 is the transfer function for the selected ARX331 model. It’s clear
that the system is a third order system. The equation indicates that system exhibits a very
high gain. The amount of the gain is ten to the power of nine. When this model was used
to design the conventional PID controller, the PID parameters values found to be very
small since the model exhibits very high gain. As a result, when applying this controller
to the experimental system, it did not work properly because the manipulated variable
value is very small which can’t drive the actuator.
Due to the above matter, modeling the system again is necessary. The new model
must introduce new approach to compensate for the matter mentioned above. As a result,
the system was modeled by using single sine input with a higher sampling time. The
new sampling time is 0.03s.
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4.4 Modeling with single sine input with 0.03s sampling time
The single sine input signal as shown in equation (4.4) was applied to the system
shown if Figure 3.13 and the output signal were observed. The sampling time period is
0.03s and operation time is 80s. The input and output signals were used for system
estimation and validation process. Figure 4.7 shows the single sine input for model
identification.
U (t) = 3sin (2*pi*2 t) (4.4)
Figure 4.7: Single sine input signal with sampling time (0.03s)
Table 4.3: Percentage fit result of single sine input signal modeling (0.03s)
Offset Best fit %
ARX331 ARX441 AMX3331
-0.1 85.27 85.30 86.96
-0.115 83.48 83.49 86.68
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-0.11 82.07 82.10 84.18
-0.12 81.20 81.23 83.95
-0.13 81.2 81.35 84.63
-0.14 77.90 78.00 78.15
-0.15 86.82 86.75 86.73
-0.16 81.04 81.11 84.70
-0.17 79.66 79.69 82.26
-0.18 87.93 87.87 88.12
Figure 4.8: Measured and simulated data output comparison with sampling time (0.03s)
Table 4.3 shows the percentage fit results of the single sine input modeling with
a sampling time of 0.03s. The table shows that most of the percentage fit is greater than
85%. The best percentage fit of 87.93% was found at -0.18 offset value. Figure 4.8
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shows the measured and simulation data outputs comparison and it show the resultant fit
percentage.
The ARX331 model structure with 87.93% was used for further analysis. The
model’s loss function is 0.0104816 and the final predication error is 0.0105776. The fit
percentage, loss function, and FPE support the model design criterion. As a result, this
model can be used for controller design purposes. Figure 4.9 shows the poles and zeros
positions of the model. All three poles of the system are inside the unit cycle which
suggests that the model in equation 4.5 is stable.
Figure 4.9: Poles and zeros unit cycle when offset value -0.18 (ARX331)
(4.5)
It’s clear that transfer function in equation (4.5) has reasonably small gain
compared to the transfer function shown in equation (4.3) which has a very big gain
value. By observing the gain values, it’s clear that transfer function in equation (4.5)
compensates for the disadvantage of transfer function shown in equation (4.3)
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consequently, transfer function in equation (4.5) was utilized for developing controllers
to improve the overall system performance.
4.5 Controller design
To improve the system performance in equation (4.5), conventional PID
controller and self tuning fuzzy PID controller were designed. The approaches taken to
design these controllers are discussed in the following section.
4.5.1 Conventional PID controller design
PID controller is very popular in the industry of pneumatic actuation because it’s
easy to use and very robust. PID controller is commonly used in industrial control as a
generic control loop feedback mechanism. System error is calculated by observing and
comparing the difference between the measured control variable and the desired set
point. PID controller can be adjusted to tune the coefficients of proportional and integral
gain with the purpose to compensate the feedback error.
Based on the ARX331 model in equation 4.5, conventional PID controller was
designed to improve the system performance. The system performance characteristics
such as rise time, overshoot, settling time, and steady state error can be improved by
tuning Kp, Ki, and Kd parameters. The following equation shows the PID mathematical
representation:
Y(t)= Kp [e(t) + Td
]
Y(t)= Kp [e(t) + Kd
] (4.6)
where: Ki = Kp / Ti ; and Kd = Kp . Td
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Figure 4.10: Conventional PID controller Simulink block diagram
Figure 4.10 shows the conventional PID controller Simulink block diagram. PID
coefficient tuning was done by using Ziegler Nichols oscillation method. This procedure
is only valid for open loop stable plant. It was carried out by using the following steps.
1. Set the true plant under proportional control with small gain.
2. Increase the gain until loop start oscillation. Linear oscillation is required
and it should be detected at the controller output.
3. Record the controller critical gain Ku to achieve constant oscillation and
the oscillation period of controller output pu.
4. Tune the PID parameters using Ziegler Nichols table shown in Table 4.4
Using the block diagram in Figure 4.10, step 1 to step 3 were followed. As a
result, Ku= 0.28 and pu= 0.02. After that, step 4 was followed. As a result, Kp = 0.168, Ki
= 1.68, and Kd = 0.0042. The resultant PID parameters after fine tuning to get a better
performance are as follows: Kp = 0.168, Ki = 1.5, and Kd = 0.0001. After that, the
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designed PID controller was applied on the experimental setup to validate the controller.
The step response of the controller was observed in simulation and real time.
Table 4.4: Ziegler Nichols’ PID controller parameters table
Controller Kp Ki Kd
P 0.5Ku - -
PI 0.45Ku 1.2 Kp/Pu -
PID 0.6Ku 2 Kp/Pu Kp Pu/8
Figure 4.11: Simulation result of PID controller step reponse.
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Figure 4.12: Experimental step response result of PID controller.
Figure 4.11 shows the simulation result of PID controller step reponse. By
analyzing the graph, the system exhabits fast respose with rise time of 0.39s and stelling
time of 1.29s. zero steady-state error was achieved with only 4.5% overshoot.
Figure 4.12 shows the experimental step response result of PID controller. From
the response graph, it was obseved that the system has fast response with 0.12s rise time
and 0.7s settling time. There is a small steady-state error of 5%, however, the system
exhibits a relatively large overshoot percentage of 27.27% The system has this big
overshoot because of the fast response dynamics the pneumatic actuator and due to the
fast input singal applied. As a result, a more adnvaced controller algothim should be
introduces to properly compensate for this errors.
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4.5.1 Self tuning Fuzzy-PID controller design
Fuzzy logic controller block diagram and self tuning fuzzy PID controller
structure were discussed at the end of chapter 3 in controller design topic. In this topic
the application of a self tuning fuzzy PID controller will be discussed.
The self tuning fuzzy PID controller rules are designed based on the pneumatic
actuator system characteristics. Therefore, the fuzzy reasoning of fuzzy sets of outputs is
gained by aggregation operation of fuzzy sets inputs and the designed fuzzy rules. The
aggregation and defuzzification method are used respectively max-min and centroid
method.
Figure 4.13 shows that in this controller system there are two inputs: error e(t)
and derivative of the error de(t) and there are three outputs of the PID controller which
are : K’p, K’i and K’d. Figure 4.13 shows Fuzzy inference block of the controller design.
Mamdani model is applied as structure of fuzzy inference with some modification to
obtain the best value for Kp, Ki and Kd.
Figure 4.13: fuzzy inference block diagram.
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Figure 4.14: Membership function of e(t)
Figure 4.15: Membership function of de(t)
Figure 4.16: Membership functions of K’p, K’I, K’d
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The membership functions of these inputs fuzzy sets are shown in Figure 4.14
and 4.15. The linguistic variable levels are assigned as NB: negative big; NS: negative
small; ZE: zero; PS: positive small; PB: positive big. These levels are chosen from the
characteristics and specification of the pneumatic actuator. The ranges of these inputs
are from -0.1 to 0.1, which are obtained from the absolute value of the system error and
its derivative through the gains, Whereas the membership functions of outputs K’p, K’i
and K’d, are shown in Figure 4.16. The linguistic levels of these outputs are assigned as
S: small; MS: medium small; M: medium; MB: medium big; B: big, where the ranges
from 0 to 1. The fuzzy rules are performed using the general rule table. Since there are 5
input variables and five output variables, the total designed fuzzy rules are 25 [20].
The PID parameters were using Ziegler-Nichols method as follows: Kp = 0.168,
Ki = 1.5, and Kd = 0.0001. Experimentally, the PID parameters vary in following ranges:
Kp∈ [0.12 0.18], Ki ∈ [1.1 1.9], Kd∈ [0.00001 0.0001].
PID parameters can be calibrated over the interval of [0 1] to get the following
equations.
=
=
=
(4.7)
Hence, = 0.06K’p+0.12; = 0.8 + 1.1; =9e-5
+0.00001 (4.8)
Using the fuzzy rule inferences in the equations 4.8, self tuning fuzzy PID
regulator subsystem block diagram was constructed as shown in Figure 4.17.
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Figure 4.17: Simulink block diagram of fuzzy PID regulator
Figure 4.18: Simulink block diagram of the system controllers.
In case of using self tuning fuzzy PID controller, The value of parameter Kp, Ki
and Kd are tuned by using signals from fuzzy logic block based on the changes in the
error between reference signals and output signals. Lastly, the simulation and
experimental results of step response were obtained and discussed.
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Figure 4.19: Simulation of self-tuning fuzzy PID controller step response result
Figure 4.20: Simulation of self-tuning fuzzy PID controller step response result
Figure 4.19 shows simulation of self-tuning fuzzy PID controller step response
result. By analyzing the graph, the system has fast response with 0.45s rise time and
1.05s settling time. The system achieved zero steady state error and zero overshoot
percentage.
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Figure 4.20 shows experimental step response result of self tuning fuzzy PID
controller. From the graph obtained, it’s clear that the system has fast response with 0.1s
rise time and 0.36s settling time. The system exhibits some steady state error of 6.25%
and it has a very small overshoot percentage of 2.22%.
Table 4.5: Simulation and experimental results of the controllers’ performance
specifications
Controller Rise
time Tr
Settling
time Ts
Overshoot
%OS
Steady
state
error %
Simulation PID 0.39s 1.29s 4.5% 0%
Fuzzy-PID 0.45s 1.05s 0% 0%
Experimental PID 0.12s 0.70s 27.27% 5%
Fuzzy-PID 0.10s 0.36s 2.22% 6.25%
From Table 4.4, it can be concluded that the system response of the pneumatic
system was improved significantly when applying conversional PID and self-tuning
Fuzzy-PID controllers. Self-tuning fuzzy-PID controller outperformed the conversional
PID controller with 2.22% overshoot only and faster response.
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CHAPTER 5
CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion
A pneumatic actuator is a mechanical device which converts the compressed air
energy into mechanical motion. The motion can be rotary or linear, depending on the
type of actuator. Many industries nowadays use pneumatic actuators in positioning,
clamping, gripping, drilling, and conveying operations in the process of manufacturing
and automation. This is due to the advantages pneumatic actuators offer over other types
of force actuators such as electromechanical and hydraulic actuators.
Although pneumatic actuators have many good attributes, achieving precise and
high-speed control of their systems is a challenge. This difficulty is due to the high-
order, time-variant actuator dynamics, and system nonlinearities like air compressibility,
static and coulomb friction, and pressure supply variations. This project presents the
process of modeling a pneumatic actuator system followed by designing controllers to
improve the system performance.
In this project, the pneumatic actuator system was modeled using system
identification toolbox. Input and output data were collected from the experimental
pneumatic actuator. The multi sine and single sine inputs where used with different
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sampling times. Two model structures were selected which are Auto Regressive
Exogenous (ARX) and Auto Regressive Moving Average Exogenous (ARMAX). Model
estimation and validation were done by analyzing residual correlation and best fit
percentage.
From the analysis of the modeling results, multi sine models were unstable
because they have one of their poles outside the unit cycle. Besides, the percentage fit is
very low and not adequate for controller design purposes. For single sine models, very
good percentage fit was achieved when the sampling time is 0.01s; however, these
models fail to be adequate for controller design due to the very high gain in the
numerator of the transfer function. The models obtained when the sampling time was
0.03s are the best to be utilized in controller design because they have good percentage
fit and are stable too. Moreover, the numerator gain is acceptable.
To improve the system performance conventional PID and self tuning fuzzy-PID
controllers are designed. The coefficients of the PID controller are tuned using trial and
error method and Ziegler- Nichols method. Conventional PID controller achieved very
good performance in simulation, where the steady-state error is zero and the transient
response is fast with small overshoot percentage. When the conventional PID controller
was applied to the experimental set-up, a big overshoot percentage was observed. As a
result, the need to compensate for this error was important by using self tuning fuzzy
PID controller.
Self-tuning fuzzy PID controller means that the three parameters Kp, Ki and Kp of
PID controller are tuned by using fuzzy tuner. The controller used the error and the
derivative of the error as input to the fuzzy logic tuner. Both simulation and
experimental results of the self tuning fuzzy PID controller performed well in terms of
steady-state response and transient response.
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The system response of the pneumatic system is improved significantly when
applying conversional PID and self-tuning Fuzzy-PID controllers. Self-tuning fuzzy-PID
controller outperformed the conversional PID controller with 2.22% overshoot only and
faster response.
5.2 Recommendations
Upon the completion of this project, there are some spaces for further
improvement. The effectiveness and accuracy of pneumatic actuator can be improved by
following the following suggestion are:
1. Optimize error of electro pneumatic regulator by controlling pressure of
the regulator valve.
2. Use neural network black box modeling.
3. Model the pneumatic actuator system with another model structure such
as Nonlinear Auto Regressive Exogenous (NLARX), Box- Jenkin, or
Output Error (OE).
4. Design a controller by using other controller such as LQR, neural
network controller or auto tuning PID controller.
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CHAPTER 6
PROJECT MANAGEMENT
6.1 Introduction
The objective of project management is to achieve all project goals with effective
project planning, organization and controlling resources within a specified time period.
The primary constrains in this project are the research scope, research time, research
budget and human resources to perform the required activity. Based on the stated
constrains, project schedule had been tabulated on a Gantt chart which gives a clear
guideline in time management of this project.
Next, cost estimation on the components is performed to insure minimal project
cost while keep working efficiently on the project to achieve the requirements. In this
process, market survey on different electronics suppliers is carried out; component
prices are then tabulated to compute the final cost.
6.2 Project Schedule
Table 6.1 shows the project Gantt chart for semester one. This table shows that
the FYP1 activity started from the very first week by choosing the project specialization
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area. The following four weeks were utilized to produce the project proposal. Table 6.1
shows that the literature review activity occupies a period of five weeks. After that, the
experimental setup was connected and checked to ensure that all the hardware
components are working properly. In the last four weeks, the FYP1 presentation and
report documentation took place.
Table 6.1: Project Gantt chart (Semester 1)
N
o Activity
week
1 2 3 4 5 6 7 8 9 1
0
1
1
1
2
1
3
1
4
1
5
1 FYP area specialization
selection
2 FYP title discussion with
the supervisor
3 Project's objectives and
scope definitions
4 Literature review on
Pneumatic systems
5 Literature review
6 Connecting and checking
the pneumatic plant
7 Understanding all the plant
components
8 Methodology definition
9 Pneumatic plant data
acquisition and collection
10 Preparation of FYP1
presentation
11 FYP1 presentation
12 Documentation and report
writing
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Table 6.2 shows the project Gantt chart for semester two. The table shows that
the FYP2 activity was started from the second week by doing the functionality testing of
the pneumatic actuator setup. In the following five weeks, the major activities were I/O
data collection and system modeling with some work on controller design.
Table 6.2 indicates clearly that controller design activity took the longest period
where it was performed starting from week five and ending in week eleven. After that,
experimental validation on the model and controller design was done to check if they
meet the design criteria. The Results were analyzed and discussed in the following three
week. Finally, the FYP2 seminar was held on the 13th
week and the thesis writing was
finished on the 18th
week.
Table 6.2: Project Gantt chart (Semester 2)
No. Activity
week
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15-
18
1 Functionality test
2 Data Collection
3 Model estimation
4 Controller design
5
Experimental
Validation
6
Analysis and
discussion
7
Seminar
preparation
8 FYP seminar
9 Thesis writing
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6.3 Cost Estimation
The pneumatic actuator system setup shown in Figure 3.3 is placed in process
control lab (P10) in faculty of electrical engineering. I did not design or fabricate the
experimental setup to do my project; however, it had already been in the lab and many
previous researchers used it in their projects. Table 6.3 shows the overall estimated cost
of the experimental setup.
Table 6.3: Main system components prices
Hardware component Cost per Piece (RM) Quantity Total (RM)
Electro-Pneumatic Regulator 909 2 1818
Communication Cable 135 3 405
LVDT (Position Sensor) 289 1 289
Pneumatic Actuator 98 1 98
Voltage Supply 12V 25 1 25
Test Table 100 1 100
Air Compressor 249 1 249
Air Tubing 50 1 50
Air Tubing 50 1 50
NI DAQ card (NI SCB-68) 350 1 350
(PCI) card (NI SCB-68) 500 1 500
Computer system 1000 1 1000
Shielded cables 300 2 600
Other Electronic Component 50
Total 5584
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REFERENCES
1. Ali, Hazem I., Et Al. "A Review of Pneumatic Actuators (Modeling and
Control)."Australian Journal of Basic and Applied Sciences 3.2 (2009): 440-454.
2. Wang, J., J. Pu, Et Al. (1999). "A Practical Control Strategy for Servo-Pneumatic
Actuator Systems." Control Engineering Practice 7(12): 1483-1488.
3. Shih, M.-C. And S.-I. Tseng (1995). "Identification and Position Control of a Servo
Pneumatic Cylinder." Control Engineering Practice 3(9): 1285-1290.
4. Pandian, Shunmugham R., Et Al. "Pressure Observer-Controller Design for
Pneumatic Cylinder Actuators." Mechatronics, IEEE/ASME Transactions on 7.4
(2002): 490-499.
5. Zhihong, R. And G. M. Bone (2008). "Nonlinear Modeling and Control of Servo
Pneumatic Actuators." Control Systems Technology, IEEE Transactions on 16(3):
562-569.
6. Wang, J., J. D. Wang, Et Al. (2004). "Identification of Pneumatic Cylinder Friction
Parameters Using Genetic Algorithms." Mechatronics, Ieee/Asme Transactions on
9(1): 100-107.
7. Nguyen Thanh, T., T. Dinh Quang, Et Al. (2011). Identification of a Pneumatic
Actuator Using Non-Linear Black-Box Model. Control, Automation and Systems
(Iccas), 2011 11th International Conference.
8. Žilić, T., D. Pavković, Et Al. (2009). "Modeling And Control Of A Pneumatically
Actuated Inverted Pendulum." Isa Transactions 48(3): 327-335.
9. Bogdan, Codres, Et Al. "Identification of A Nonlinear Pneumatic Servo System
Using Modular Neural Networks."
10. Ning, Y., M. Betemps, Et Al. (1991). A Servocontrolled Pneumatic Actuator for
Small Movement-Application to an Adaptive Gripper. Advanced Robotics, 1991.
'Robots In Unstructured Environments', 91 Icar., Fifth International Conference.
11. Filipovic, V., N. Nedic, Et Al. (2011). "Robust Identification Of Pneumatic
ServoActuators In The Real Situations." Forschung Im Ingenieurwesen 75(4): 183-
196.
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12. Chiang, Chiang-Cheng, and Mon-Han Chen. "Robust Adaptive Fuzzy Control of
Uncertain Nonlinear Time-Delay Systems with an Unknown Dead-Zone." Fuzzy
Systems, 2008. Fuzz-Ieee 2008. (IEEE World Congress on Computational
Intelligence). Ieee International Conference On. IEEE, 2008.
13. Chillari, S., S. Guccione, And G. Muscato. "An Experimental Comparison between
Several Pneumatic Position Control Methods." Decision and Control, 2001.
Proceedings of the 40th IEEE Conference On. Vol. 2. IEEE, 2001.
14. Yamazaki, M. And S. Yasunobu (2007). An Intelligent Control for State-Dependent
Nonlinear Actuator and Its Application to Pneumatic Servo System. Sice, 2007
Annual Conference.
15. Sunar, N. H., M. F. Rahmat, Et Al. (2013). Identification and Self-Tuning Control of
Electro-Pneumatic Actuator System with Control Valve. System Engineering and
Technology (ICSET), 2013 IEEE 3rd International Conference.
16. Malaysia, Melaka. "Non-Linear Modeling and Cascade Control of an Industrial
Pneumatic Actuator System." Australian Journal of Basic and Applied Sciences 5.8
(2011): 465-477.
17. Rahmat, M. F., Et Al. "Identification and Non-Linear Control Strategy for Industrial
Pneumatic Actuator." International Journal of Physical Sciences 7.17 (2012): 2565-
2579.
18. Lai, W. K., M. F. Rahmat, and N. Abdul Wahab. "Modeling and Controller Design
of Pneumatic Actuator System with Control Valve." International Journal on Smart
Sensing and Intelligent System 5.3 (2012): 624-644.
19. Sunar, N. H., M. F. Rahmat, Et Al. (2013). Application of Optimization Technique
for PID Controller Tuning In Position Tracking Of Pneumatic Actuator System.
Signal Processing and Its Applications (CSPA), 2013 IEEE 9th International
Colloquium.
20. Zulfatman and M.F. Rahmat, “Application of Self-tuning Fuzzy PID Controller on
Industrial Hydraulic Actuator Using System Identification Approach”, International
Journal on Smart Sensing and Intelligent System. Vol.2. No 2, 2009.
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APPENDIX A
Electro-pneumatic Regulator
ITV1000/2000/3000
Standard Specifications
Straight type Right angle type
JIS Symbol Rated pressure
(MP
a)
Out
put p
ress
ure
This range is outside of the control (output).
0.005 MPa 0
0 100 Input signal (%F.S.)
Graph (1) Input/output characteristics chart
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ITV101_ ITV103_ ITV105_
Model ITV201_ ITV203_ ITV205_
ITV301_ ITV303_ ITV305_
Minimum supply pressure Set pressure +0.1 MPa
Maximum supply pressure 0.2 MPa 1.0 MPa
Set pressure range Note 1) 0.005 to 0.1 MPa 0.005 to 0.5 MPa 0.005 to 0.9 MPa
Voltage 24 VDC 10%, 12 to 15 VDC
Power supply Current Power supply voltage 24 VDC type: 0.12 A or less
consumption Power supply voltage 12 to 15 VDC type: 0.18 A or less
Current type Note 2) 4 to 20 mA, 0 to 20 mA (Sink type)
Input signal Voltage type 0 to 5 VDC, 0 to 10 VDC
Preset input 4 points
Input Current type 250 Ω or less
Voltage type Approx. 6.5 kΩ
impedance
Preset input
Approx. 2.7 kΩ
Note 3) Analog output
1 to 5 VDC (Load impedance: 1 kΩ or more)
Output signal 4 to 20 mA (Sink type) (Load impedance: 250 Ω or less)
(monitor
NPN open collector output: Max. 30 V, 30 mA
output) Switch output
PNP open collector output: Max. 30 mA
Linearity Within 1% (full span)
Hysteresis Within 0.5% (full span)
Repeatability Within 0.5% (full span)
Sensitivity Within 0.2% (full span)
Temperature characteristics Within 0.12% (full span)/C
Output pressure Accuracy 3% (full span)
display Minimum unit MPa: 0.01, kgf/cm2: 0.01, bar: 0.01, PSI: 0.1 Note 4), kPa: 1
Ambient and fluid temperature 0 to 50C (with no condensation)
Enclosure IP65
ITV10__ Approx. 250 g (without options)
Weight ITV20__ Approx. 350 g (without options)
ITV30__ Approx. 645 g (without options)
Note 1) Please refer to “Graph (1)”, relation to the differences between the set pressure and input. Additionally, refer to page 14-8-29 for the set pressure range by units of standard measured pressure. Additionally, refer to page 14-8-29 as maximum set pressure differs on unit of standard measure.
Note 2) 2-wire type 4 to 20 mA is not available. Power supply voltage (24 VDC or 12 to 15 VDC) is required. Note 3) Select either analog output or switch output. Further, when switch output is selected, select either
NPN output or PNP output. Note 4) The minimum unit for ITV205_ is 1PSI. Note 5) The above characteristics are confined to the static state. When air is consumed on the output side, the pressure may fluctuate.
3 ∗ Switch output/PNP output T ∗ NPTF 2 1/4 (1000, 2000, 3000 type) B ∗ Flat bracket
4 ∗ Analog output 4 to 20 mA (Sink type) F ∗ G 3 3/8 (2000, 3000 type) C ∗ L-bracket
∗ Option ∗ Option 4 1/2 (3000 type) ∗ Option
14-8-14
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Electro-pneumatic Regulator Series ITV1000/2000/3000
rSpacer SMC REGULATOR
ITV20__
ITV2000
App
rox.
189
Ap
prox
. 133
eL-bracket
178
qAF30 wAFM30
REGU LAT OR ITV30__
ITV3000
App
rox.
243
Appr
ox. 1
72
234
qAF40 wAFM40
Combinations Standard Combination Combination
specifications possible not possible
∗ ITV10__ models are not applicable.
Sym
bol Applicable model
Specifications ITV20__
ITV30__
Set pressure max. 0.1 MPa 1
Stan
dards
pecif
ica
tions
Set pressure max. 0.5 MPa 3
Set pressure max. 0.9 MPa 5
Connection Rc 1/4 02
Connection Rc 3/8 03
Connection Rc 1/2 04
Acces- Bracket B
sories Bracket C
Optio
nalsp
ecific
ation
s
Connection NPT1/4 N02
Connection NPT3/8 N03
Connection NPT1/2 N04
Connection G 1/4 F02
Connection G 3/8 F03
Connection G 1/2 F04
Modular Products and Accessory Combinations
∗ ITV10__ models are not applicable.
Applicable products and accessories Applicable model
ITV20__
ITV30__
q Air filter AF30 AF40
w Mist separator AFM30 AFM40
e L-bracket B310L B410L
r Spacer Y30 Y40
t Spacer with L-bracket (e + r) Y30L Y40L
F.R.L. AV AU AF AR IR VEX AMR ITV IC VBA VE_ VY1 G
Accessory (Option)/Part No.
Description Part no. Dimensions
ITV10__ ITV20__ ITV30__ Flat bracket
Flat bracket P3020114 100
(Mounting thread is not included.)
L-bracket INI-398-0-6
(Mounting thread is not included.)
5 2 4 0 2 2
Cable
conn
ector Straight TM-4DSX3HG4
type 3 m
PPA
AL
L-bracket
7
25
1 5 4
x
R
30 3 2.3
.
5
36
50
Right angle TM-4DLX3HG4 22
type 3 m
4 x ø7
_36
40
84
60
12
1.6
20
8 x ø4.5 50
4 x ø5.5
40
36
22
14
7 5
70
22
36
4 0
8 x ø4.5 4 x ø5.5
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Series ITV1000/2000/3000
Working Principle When the input signal rises, the air supply solenoid valve q turns ON, and the exhaust solenoid valve w turns OFF. Therefore, supply pressure passes through the air supply solenoid valve q and is applied to the pilot chamber e. The pressure in the pilot chamber e increases and operates on the upper surface of the diaphragm r. As a result, the air supply valve t linked to the diaphragm r opens, and a portion of the supply pressure becomes output pressure. This output pressure feeds back to the control circuit i via the pressure sensor u. Here, a correct operation functions until the output pressure is proportional to the input signal, making it possible to always obtain output pressure proportional to the input signal.
Working Principle Diagram Pressure display
Power supply i Control Output signal
Input signal circuit
Pressure display
Power supply
q Air supply w Exhaust
i Control Output signal solenoid solenoid
Input signal circuit valve
valve
EXH
q Air supply w Exhaust
solenoid
solenoid u Pressure
valve
valve
r Diaphragm sensor
EXH e Pilot
y Exhaust chamber
valve
u Pressure sensor
r Diaphragm e Pilot chamber t Supply EXH
valve
t Supply valve
SUP OUT SUP OUT
EXH
ITV1000 ITV2000, 3000
Block diagram Input signal
i Control circuit
Supply pressure q Air supply solenoid valve
Output pressure r Diaphragm Pilot valve
w Exhaust solenoid valve u Pressure sensor
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Electro-pneumatic Regulator Series ITV1000/2000/3000 Series ITV101_
Linearity Hysteresis Repeatability
1.0 1.0
0.10
Out
put d
evia
tion
fact
or
(%F.
S.)
Out
Out
put d
evia
tion
fact
or
(%F.
S.)
0.5 0.5
Set
pre
ssur
e (M
Pa)
0.08
0.06 0.0 Return
0.0
0.04
–0.5 –0.5
0.02
0.00 25 50 75 100
–1.0 25 50 75 100
–1.0 2 4 6 8 10
0 0 0
Input signal (%F.S.) Input signal (%F.S.) Repetition
Pressure Characteristics
Set pressure: Flow Characteristics
Supply pressure: Relief Flow Characteristics
Supply pressure:
0.05 MPa 0.2 MPa 0.2 MPa
Out
put d
evia
tion
fact
or (%
F.S
.)
1.0 0.15 0.25
0.5
Set
pre
ssur
e (M
Pa)
0.20
Set
pre
ssur
e (M
Pa)
Set point 0.10
0.15
0.0
0.05 0.10
–0.5 0.05
–1.0
0.3
0 20 40 60 80 100
0.000
0.0 0.1 0.2 20 40 60 80
Supply pressure (MPa) Flow rate ( /min (ANR)) Flow rate ( /min (ANR))
F.R.L. AV AU AF AR IR VEX AMR ITV IC
Series ITV201_
Linearity Hysteresis Repeatability
0.10 1.0 1.0
0.09
Out
put d
evia
tion
fact
or
(%F.
S.)
Out
put d
evia
tion
fact
or
(%F.
S.)
0.08 0.5 Out 0.5
Set
pre
ssur
e (M
Pa)
0.07
0.06 0.0
0.0
0.05
0.04 Return
0.03
–0.5 –0.5
0.02
0.01
0.00 25 50 75 100
–1.0 25 50 75 100
–1.0 2 4 6 8 10
0 0 0
Input signal (%F.S.) Input signal (%F.S.) Repetition
VBA VE_ VY1 G PPA AL
Pressure Characteristics
Set pressure:
0.05 MPa
(%F.
S.) 1.0
0.5
fact
or Set point
0.0
devi
atio
n
–0.5
Out
put
–1.0
0.0 0.1 0.2 0.3
Supply pressure (MPa)
Flow Characteristics Supply pressure:
0.2 MPa
0.15
(MPa
)
0.10
pres
sure
0.05
Set
0 200 400 600
Flow rate ( /min (ANR))
Relief Flow Characteristics Supply pressure:
0.2 MPa
0.25
(MP
a) 0.20
0.15
Set p
ress
ure
0.10
0.05
0.000 200 400 600 800
Flow rate ( /min (ANR))
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Electro-pneumatic Regulator Series ITV1000/2000/3000
Dimensions ITV10__ Flat bracket Note) Do not attempt to rotate, as the cable connector does not turn.
Cable connector (4-wire) Cable connector (4-wire)
Right angle type Straight type
_50 4 x ø7
Mounting hole ( 3 1 )
SMCE
P REGULATOR SMC E
P RE GULAT OR
RESET
52
SET
UNLOCK LOCK ITV1000 ITV1000
Setting part
84
100
12.5 M12 x 1
Cable connection threads
( 1 1 )
SMCE
P REGULATOR
MPa
ITV10
INPUT 0~10VDC
Rc1/8
OUTPUT 0.005~0.5MPa
M3 x 0.5 MADE IN JAPAN GY
7 1
Exhaust port
Solenoid valve
EXH Solenoid valve EXH
G 2 OUT
SUP (1) OUT (2)
19.5 EXH (3)
1 1
1 2
40 Flat bracket 2 x Rc1/8, 1/4
P3020114
Port size
(Optional)
4 x M4 x 0.7 thread depth 6 mm through Mounting hole
L-bracket
F.R.L. AV AU AF AR IR VEX AMR ITV IC VBA VE_ VY1 G PPA AL
G
15 .5
R3
2 5 7
(10) 30
(7) 36 L-bracket
INI-398-0-6
(Optional)
2 OUT
22 2.3
45 2 x Rc1/8, 1/4 SUP port, OUT port
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APPENDIX B
KTC / KTF Linear sensor 75-1000 mm Potentiometric transducer with conductive track suitable for measurment, monitoring and control of mechanical strokes. Critical in providing a smooth output, mechanically dependent on the stable glide of the shaft and wiper on the element’s surface. Applications such as industrial controls, robotics, process systems or replacement of a linear voltage differential trans- former (LVDT) are ideal uses for this versatile, reliable model.
Features:
Case ... Brushes ... Resistance track ... Control rod ... Resolution ... Repeatability ... Life time ...
Electrical connections ...
Temperature range ...
.Anodised aluminium ..Noble metal
.. Conductive plastic on polymer base ..Stainless steel
.. Infinitie .within 0,013 mm
..>25x106 meters or >100x106 cycles ...4-pole connector to DIN
..43650 ISO 4400 . -55ºC- +125ºC
Output options
Contact Standard 4-20 mA 1 - supply - supply 2 Signal 0-V+- 4-20 mA 3 + supply + 15-35 V
Mechanical dimensions KTC
Max supply voltage at 70°C .................................................... .40 VDC Recommended cursor current ... .......................................... .. <1mMechanical dimensions KTF Rod end bearing (KTC-01)
Coupling join
KTC KTC7 100 150 225 300 375 450 525 600 750 900 KTF100
Total el. travel (T.E) mm 76 102 150 229 305 381 457 533 610 762 914 1016 -
Active el. travel (A.E) mm 75 100 150 226 302 378 455 531 607 759 912 1013
Resistance (±20%) k 2,5 3,4 5,0 2,4 3,2 4,0 4,8 5,6 6,4 8,0 9,6 10,7
Independent linearity ±% 0,07 0,07 0,07 0,07 0,07 0,07 0,05 0,05 0,05 0,05 0,05 0,05
Mechanical travel (M.T) mm Dimensions KTC (A) mm
79 104 155 231 307 384 139 164 215 291 367 444
460 536 612 765 917 1019 520 596 672 825 977
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