1 dan o. popa, ee 1205 intro. to ee 1 systems concepts dan popa, ph.d., associate professor...

1Dan O. Popa, EE 1205 Intro. to EE 1

Systems Concepts

Dan Popa, Ph.D., Associate [email protected], http://ngs.uta.edu

• Systems Approach and Related Concepts• Modeling: Physical, mathematical • System identification, block diagrams, subsystems, modules,

interconnection• Input/output, environmental effects, linear-nonlinear, dynamic,

causal-noncausal• Examples of complex robotic systems

mailto:[email protected]

http://ngs.uta.edu/


Signals and Systems

– Signal: • Any time dependent physical quantity• Electrical, Optical, Mechancal

– System:• Object in which input signals interact to

produce output signals.• Some have fundamental properties that make

it predictable: – Sinusoid in, sinusoid out of same frequency (when

transients settle)– Double the amplitude in, double the amplitude out

(when initial state conditions are zero)?

x(t)

u(t) y(t)


Signal Classification

– Continuous Time vs. Discrete Time

• Telephone line signals, Neuron synapse potentials

• Stock Market, GPS signals

– Analog vs. Digital• Radio Frequency (RF)

waves, battery power• Computer signals, HDTV

images


Signal Classification

– Deterministic vs. Random• FM Radio Signals• Background Noise Speech

Signals

– Periodic vs. Aperiodic• Sine wave• Sum of sine waves with non-

rational frequency ratio


System Classification

– Linear vs. Nonlinear• Linear systems have the property of

superposition– If U →Y, U1 →Y1, U2 →Y2 then

» U1+U2 → Y1+Y2» A*U →A*Y

• Nonlinear systems do not have this property, and the I/O map is represented by a nonlinear mapping.

– Examples: Diode, Dry Friction, Robot Arm at High Speeds.

– Memoryless vs. Dynamical• A memoryless system is represented by a

static (non-time dependent) I/O map: Y=f(U). – Example: Amplifier – Y=A*U, A- amplification factor.

• A dynamical system is represented by a time-dependent I/O map, usually a differential equation:

– Example: dY/dt=A*u, Integrator with Gain A.

Mandelbrot set, a fractal image, result of a Nonlinear Discrete System Zn+1=Zn²+C

0

0)sin(

2

2

2

2

L

g

dt

d

L

g

dt

d Exact Equation, nonlinear

Approximation around vertical equilibrium, linear


System Classification

– Time-Invariant vs. Time Varying• Time-invariant system parameters do not change over time. Example: pendulum, low

power circuit• Time-varying systems perform differently over time. Example: human body during exercise.

– Causal vs. Non-Causal• For a causal system, outputs depend on past inputs but not future inputs. Examples:

most engineered and natural systems• A non-causal system, outputs depend on future inputs. Example: computer simulation

where we know the inputs a-priori, digital filter with known images or signals.

– Stable vs. Unstable• For a stable system the output to bounded inputs is also bounded. Example:

pendulum at bottom equilibrium• For an unstable system the ouput diverges to infinity or to values causing permanent

damage. Example: short circuit on AC line.


System Modeling

• Building mathematical models based on observed data, or other insight for the system.– Parametric models (analytical): ODE, PDE– Non-parametric models: graphical models - plots, look-

up cause-effect tables– Mental models – Driving a car and using the cause-

effect knowledge– Simulation models – Many interconnect subroutines,

objects in video game


Types of Models

• White Box – derived from first principles laws: physical,

chemical, biological, economical, etc.– Examples: RLC circuits, MSD mechanical models

(electromechanical system models).• Black Box

– model is entirely derived from measured data– Example: regression (data fit)

• Gray Box – combination of the two


White Box Systems: Electrical

• Defined by Electro-Magnetic Laws of Physics: Ohm’s Law, Kirchoff’s Laws, Maxwell’s Equations

• Example: Resistor, Capacitor, Inductor

u

Riu

i

C

ui

L


RLC Circuit as a System

Kirchoff’s Voltage Law (KVL):

u1

L

C

R

uu3

u2RLCq(t)

u(t) i(t)


White Box Systems: Mechanical

Newton’s Law:

M

K

BF MSD

x(t)

F(t) x(t)

Mechanical-Electrical Equivalance:

F (force) ~V (voltage)x (displacement) ~ q (charge)M (mass) ~ L (inductance)B (damping) ~ R (resistance)1/K (compliance) ~ C (capacitance)


White-Box vs. Black-Box Models

Newton-Euler Law:

LawnMowerx,y,θ

ω_r(t), ω_l(t) X(t), Y(t) Θ(t)


Grey-Box Models


White Box vs Black Box ModelsWhite Box Models Black-Box Models

Information Source First Principle Experimentation

Advantages Good ExtrapolationGood understandingHigh reliability, scalability

Short time to developLittle domain expertise requiredWorks for not well understood systems

Disadvantages Time consuming and detailed domain expertise required

Not scalable, data restricts accuracy, no system understanding

Application Areas Planning, Construction, Design, Analysis, Simple Systems

Complex processesExisting systems

Start to understand simple white continuous time models which are linearEventually deal with grey-box or black-box models in real-life


Linear vs. Nonlinear

• Why study continuous linear analysis of signals and systems when many systems are nonlinear in practice?– Linear systems have generic, predictable performance.– Nonlinear systems can be approximated and transformed

into linear systems.– Some techniques for analysis of nonlinear systems are

based on linear methods– If you don’t understand linear dynamical systems you

certainly can’t understand nonlinear systems


Application Areas for Systems Thinking

• Classical circuits & systems (1920s – 1960s) (transfer functions, state-space description of systems).

• First engineering applications: military - aerospace 1940’s-1960s

• Transitioned from specialized topic to ubiquitous in 1980s with EE applications to:– Electronic circuit design– Signal and image processing

• Networks (wired, wireless), imaging, radar, optics.

– Control of dynamical systems• Feedback control, prediction/estimation/identification of systems, robotics, micro

and nano systems


Diagram Representation of Systems

Top

Bottom 1 Bottom 2 Bottom 3

Middle

Graph Node 1

Graph Node 3

Graph Node 5

Graph Node 4

Graph Node 2

Hierarchical Diagram: Organizations

Undirected Graph: Networks Flowchart: Procedures, Software


System Simulation Software

• Matlab Simulink– http://www.mathworks.com/support/2010b/simu

link/7.6/demos/sl_env_intro_web.html

• National Instruments Labview– http://www.ni.com/gettingstarted/labviewbasics/

environment.htm

http://www.mathworks.com/support/2010b/simulink/7.6/demos/sl_env_intro_web.html

http://www.mathworks.com/support/2010b/simulink/7.6/demos/sl_env_intro_web.html

http://www.ni.com/gettingstarted/labviewbasics/environment.htm

http://www.ni.com/gettingstarted/labviewbasics/environment.htm


EE-Specific Diagrams

• Block Diagram Model: – Helps understand flow of information (signals) through a complex system– Helps visualize I/O dependencies– Equivalent to a set of linear algebraic equations.– Based on a set of primitives:

Transfer Function Summer/Difference Pick-off point

• Signal Flow Graph (SFG): – Directed Graph alternative

H(s)U(s) Y(s)

+

+

U2

U1 U1+U2 U U

U


EE-Specific Diagrams: Signal Flow Graph (SFG – Directed Graph)

2-port circuit SFG Multi-loop Control SFG


EE-Specific Diagrams: Block Diagram Simplification Rules


EE-Specific Diagrams: Block Diagram Reduction Rules


Robots as Complex Systems

G. Bekey definition: an entity that can sense, think and act.Extensions: communicate, imitate, collaborate Classification: manipulators, mobile robots, mobile manipulators.

Sense Think Act

Robot

25Dan O. Popa, EE 1205 Intro. to EE

Research in Multiscale Robotics atNext Gen Systems (NGS) Group

Robotics

Control Systems

Manufacturing & Automation

Established Technologies Emerging Technologies

Micromanufacturing Microrobotics Microassembly MicropackagingSensors & ActuatorsNanoManufacturing

Microsystems & MEMS

Nanotechnology

Biotechnology

Small-scale Robotics & Manufacturing

Modeling & Simulation

Control Theory

Algorithms

Tools and Fundamentals

Sensor networks

Surgical robotics

Human-like robots

Distributed systems

New applicationsfor small-scale systems


NGS Research

• Micro and Nano Robotics– Manufacturable Micro and Nano Robotics

• Automated MEMS Assembly and Packaging– Mobile Microrobotics

• Sub-Millimeter size robots powered by ambient fields

• Next Generation Robotics for Healthcare– Assistive Robotics

• Treatment of cognitive and motor disabilities (Autism, CP) using Advanced Human-Robot Interaction (HRI)

– Microrobotics for healthcare application (in-vivo or in-vitro manipulation and process tools)

• Examples from recent projects – Micro Robotic Factories– UTA Microrobotics Team – Zeno and Neptune Assistive Robots


M³ - Multiscale (Macro-Micro) Robotic Assembly Cell

Multiscale Robotic Workcell: work volume of approximately O(1 m3), robots with dimensions of O(10-1~10-2m), handles parts of size O(10-2~10-4m), and achieves accuracies in the scale of O(10-4~10-6

m). Four precision robots sharing a common workspace, with multiple

end-effectors: microgrippers, zoom microscope, laser for solder reflow.

Control through Labview and NI motion control products

Laser solder reflow (delivery optics) – 3DOF

Zoom-camera system – 2DOF

Gripper Manipulator 4DOF

Tool tray with quickchange end-effectors

Parts trayFine manipulator3DOF

Hot plate for die attach

Packaged MOEMS device using the M³ for US Navy Schematic and Control System Diagram of M³

D.O. Popa, R. Murthy, A. N. Das, “M3- Deterministic, Multiscale, Multirobot Platform for Microsystems Packaging: Design and Quasi-Static Precision Evaluation,” in IEEE Transactions on Automation Science and Engineering (T-ASE), April 2009.


N³ - Wafer Scale Microfactory (Micro-Nano)

“From a few robots+controllers to many µrobots via assembly and die bonding”

D. O. Popa, R Murthy, A.N. Das, “M³, μ³, and N³: Top-down, Deterministic Macro to Nano Robotic Factories with Yield and Speed Adjusted Precision Metrics,” in Proc. of 2008 Int’l Workshop on Microfactories (IWMF ’08), Evanston, Illinois, Oct. 6-8, 2008.

Controller + Robot µparts, nparts in

µparts, nparts in

Assemblies out

µrobot MEMS dies

µcontroller IC dies


AFAM: A Millimetric Assembled 4 axis Micromanipulator for N³

Microrobot volume:2mm x 3mm x 1mm Work Volume: 50m x 50m x 75mActuation: Electrothermal Transmission: XY – direct drive

- cable drivenAll components fabricated using DRIE on 50~100 microns (device) SOI.Arm is detethered and assembled out-of-plane using passive jammer.Cable (30 m Cu wire) is cut to required length and assembled.

R. Murthy, D. O. Popa, “A Four Degree Of Freedom Microrobot with Large Work Volume”, in proc. of IEEE ICRA ‘09, Kobe, Japan, May 2009


ARRIpede: a Millimetric Microcrawler

System Specifications:•Volume = 1.7cm X 1.7cm X 1 cm• Weight ~ 4g (with battery)• Velocity=~2mm/s•Max Payload~9g•Resolution of 20~30nm•Repeatability better than 12 μm•Continous operation: 10 minutes at max speed, 100 minutes at .1x max speed Rakesh Murthy, A .N. Das, D. O. Popa, “Nonholonomic Control of an Assembled

Microcrawler,” in Proc. Of 9th International IFAC Symposium on Robot Control, Gifu, Japan, September 2009.


N³ - Wafer Scale Microfactory for Nanotechnology


Making the Microfactory by Automated 3D Microassembly

Control Challenges:-Larger number of robots

- Measurement uncertainty, measurement range,

- Time delays

- Fewer embedded sensors, low SNR

- Manufacturing uncertainty, inacurate robot models)

- Environmental effects (stiction, temperature)


NIST Microbotics Challenge 2011• Hosted at IEEE International Conference on Robotics and Automation, Shanghai, China, May 10, 2011. • 7 Qualified Teams: France (FEMTO-ST), Italy (IIT), Univ. of Waterloo (CA), 4 US Universities (Stevens,

Hawaii, Maryland, UTA• Maximum robot size: 600 microns sphere.

MobilityChallenge

MicroAssemblyEvent

Vibration and Laser Actuated

UTA Microrobots, 2011


UTA Vibot Control Using National Instruments PXI-8196

• Microrobot pose (x, y, θ) from NI-1742 Smart Camera• Exchange of pose data with the control interface VI via shared variables• User control of square wave output through PXI-5201 Arbitrary Waveform

Generator (AWG). Output frequency to piezoelectric actuator. PXI 7831 FPGA RIO• Data logging via control interface VI• UTA Microrobotics Team video

square waveamplitude & frequency

PXI-8196 controllerrobot posex, y, θ

PZT Actuator

arena andmicrorobot

image

user control

control interface VI

http://www.youtube.com/watch?v=GZMUZ1aeLD8


Advanced Human-Robot Interaction for Assistance to Children with Special Needs Interaction through advanced vision, motion control, gestures and compliant robotic skin. Interaction though Wii Remotes, Neural Brain interfaces and iPad devices. Collaboration with Heracleia Lab, UTA, Hanson Robotics Inc., UNTHSC

MicroRobotics for In-Vivo Diagnosis and Treatment Mobile microrobotics: Sub-mm size robots powered and controlled wirelessly through wireless energy fields such as EM,

vibration, or laser. Micro and Nano Assembly of Surgical Instruments for medical procedures in hard to reach places (eye, ear, brain).

Next Generation Robots for Healthcare

Interface

Wii Remote

EPOC neuroheadset

PC

VS Code

Neptune Mobile Manipulator

Encoders

Force Sensors

iPad

Camera

Recorded Results Patient

WiFi

Blue

toot

h

Advanced control for Zeno Robokind (by Hanson Robotics, Inc.) generating facial expressions and maintaining eye contact with NGS programmers.

Neptune Mobile Manipulator interfaced to human users through Wii Remote, Neural Headband, and Cameras, and provides assistance to users with motor impairments.


Realistic & Intuitive Human-Robot

Interaction

Co-botics w/ Physical Interaction

Real-Time Visual Feedback and Facial

Expressions

Advanced Human-Robot Interfaces

Advanced Human Robot Interaction

Zeno Video

Neptune Control through Neural Headband

Robot Touch HRI

Visual HRI


Conclusion

- Abstraction is the basis for system level thinking. Abstraction requires advanced mathematics, and it is especially required of Electrical and Computer Engineers.

- Systems are composed of building blocks used to generalize and manage complexity.

- Modeling and simulations are important building tools in systems-level approaches.

- System-level concepts are important tools in your engineering education.- System-level thinking helps manage complexity of present-day

technology, economics, society.- System-level approach originating in EE are a great advantage in

interdisciplinary projects with all your future colleagues from other departments.

1 dan o. popa, ee 1205 intro. to ee 1 systems concepts dan popa, ph.d., associate professor...

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