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Experimental Control Science Methodology, Algorithms, Solutions

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Experimental Control Science Methodology, Algorithms, Solutions. Zhiqiang Gao, Ph.D. Center for Advanced Control Technologies Cleveland State University December 24, 2004. http://cact.csuohio.edu. Outline. Introduction Questions Research Direction Methodology - PowerPoint PPT Presentation
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1 Experimental Control Science Methodology, Algorithms, Solutions Zhiqiang Gao, Ph.D. Center for Advanced Control Technologies Cleveland State University December 24, 2004 http://cact.csuohio.edu
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Page 1: Experimental Control Science Methodology, Algorithms, Solutions

1

Experimental Control Science

Methodology, Algorithms, Solutions

Zhiqiang Gao, Ph.D. Center for Advanced Control Technologies

Cleveland State UniversityDecember 24, 2004

http://cact.csuohio.edu

Page 2: Experimental Control Science Methodology, Algorithms, Solutions

2

Outline• Introduction

• Questions

• Research Direction

• Methodology

• Active Disturbance Rejection

• Advanced Technologies

• Take Aways

• Open Problems

Page 3: Experimental Control Science Methodology, Algorithms, Solutions

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From Applied Research to

Advanced Technologies

Center for Advanced Control Technologies

http://cact.csuohio.edu

Page 4: Experimental Control Science Methodology, Algorithms, Solutions

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Center for Advanced Control Technologies

Zhiqiang Gao, Director

Sridhar Ungarala, Chemical Engineering

Daniel Simon, Embedded Control Systems, Electrical Engineering

Paul Lin, Mechanical Engineering.

Yongjian Fu, Software Engineering

Sally Shao, Mathematics

Jack Zeller, Engineering Technology

Page 5: Experimental Control Science Methodology, Algorithms, Solutions

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Past Projects• Temperature Regulation• Intelligent CPAP/BiPAP • Motion Indexing• Truck Anti-lock Brake System• Web Tension Regulation• Turbine Engine Diagnostic• Computer Hard Disk Drive• Stepper Motor Field Control• 3D Vision Tire Measurement• Digitally Controlled Power Converter

Page 6: Experimental Control Science Methodology, Algorithms, Solutions

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Sponsors• NASA• Rockwell Automation• Kollmorgen• ControlSoft• Federal Mogul• AlliedSignal Automotive• Invacare Co.• Energizer• Black and Decker• Nordson Co. • CAMP

Page 7: Experimental Control Science Methodology, Algorithms, Solutions

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NASA Intelligent PMAD Project

Page 8: Experimental Control Science Methodology, Algorithms, Solutions

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Web Tension Regulation

Page 9: Experimental Control Science Methodology, Algorithms, Solutions

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Truck Anti-lock Brake System

Page 10: Experimental Control Science Methodology, Algorithms, Solutions

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Turbofan engine

Page 11: Experimental Control Science Methodology, Algorithms, Solutions

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A Non-isothermal CSTR

• CV: product concentration CA

• MV: Coolant flowrate qc

• Difficulties: – Strong nonlinearity– Time varying

parameters: c(t) h(t) (catalyst deactivation and heat transfer fouling)

11

0

0

( ) exp ( )

( ) exp ( )

1 exp ( )

AAf A A c

f A cp

c pcc h cf

p c pc

dC q EC C k C tdt V RT

dT q H ET T k C tdt V C RT

C hAq t T TC V q C

Coolant

Feed

q c

Product, CA

AT

AC

Page 12: Experimental Control Science Methodology, Algorithms, Solutions

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Nonlinear 3-Tank Fault Id. Problem

6 possible faults 2 inputs 3 outputs

Page 13: Experimental Control Science Methodology, Algorithms, Solutions

13

CACT Mission• Define, Articulate, Formulate

Fundamental Industrial Control Problems

• Solutions and Cutting Edge Technologies

• Performance and Transparency

• Synergy in Research and Practice

Page 14: Experimental Control Science Methodology, Algorithms, Solutions

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Outline• IntroductionIntroduction

• Questions

• Research Direction Research Direction

• MethodologyMethodology

• Active Disturbance RejectionActive Disturbance Rejection

• Advanced TechnologiesAdvanced Technologies

• Take AwaysTake Aways

• Open ProblemsOpen Problems

Page 15: Experimental Control Science Methodology, Algorithms, Solutions

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Questions

• What is control & where does it belong?

• What is a good controller & how to find it?

• Does a theory-practice gap exist? Why?

• Can theoretical advance be driven by practice?

• What is the most fundamental control problem?

Page 16: Experimental Control Science Methodology, Algorithms, Solutions

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How do we describe it?

• An Art of Practice? • Hidden Technology?• Mathematics? • Engineering Science?• Control Science? • Natural Science?

Page 17: Experimental Control Science Methodology, Algorithms, Solutions

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Where does control belong?

• Electrical Engineering• Mechanical Engineering• Chemical Engineering• Aerospace Engineering• System Engineering• Mathematics• Biology?

Page 18: Experimental Control Science Methodology, Algorithms, Solutions

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Is there a theory-practice gap?

Control Theory

Engineering Problem Solving

?

Page 19: Experimental Control Science Methodology, Algorithms, Solutions

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Can theory be driven by practice?

New Theory

?

Engineering Problem Solving

Page 20: Experimental Control Science Methodology, Algorithms, Solutions

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Outline• IntroductionIntroduction

• QuestionsQuestions

• Research Direction

• MethodologyMethodology

• Active Disturbance RejectionActive Disturbance Rejection

• Advanced TechnologiesAdvanced Technologies

• Take AwaysTake Aways

• Open ProblemsOpen Problems

Page 21: Experimental Control Science Methodology, Algorithms, Solutions

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Theory vs. Practice

A Historical Perspective

Page 22: Experimental Control Science Methodology, Algorithms, Solutions

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Looking back

• PID (N. Minorsky) 1922 • Nyquist 1932• Bode 1940 • Kalman 1961 …• Ho 1982• Han 1989/1999

Page 23: Experimental Control Science Methodology, Algorithms, Solutions

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Classical Control Era

ControlPractice

ControlResearch

ControlTheory

Mathematics

Page 24: Experimental Control Science Methodology, Algorithms, Solutions

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Modern Control Era

ControlPractice

ControlResearch

ControlTheory

MathematicsResearch

Theory

unobservable

uncontrollable

Page 25: Experimental Control Science Methodology, Algorithms, Solutions

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<The Structure of Scientific Revolutions> by Thomas S. Kuhn

Research:

• A strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education

• Most scientists are engaged in mopping up operations

Science:

• Suppresses fundamental novelties because they are necessarily subversive of its basic commitments.

• Predicated on the assumption that the scientific community knows what the world is like.

Page 26: Experimental Control Science Methodology, Algorithms, Solutions

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Outline• IntroductionIntroduction

• QuestionsQuestions

• Research DirectionResearch Direction

• Methodology

• Active Disturbance RejectionActive Disturbance Rejection

• Advanced TechnologiesAdvanced Technologies

• Take AwaysTake Aways

• Open ProblemsOpen Problems

Page 27: Experimental Control Science Methodology, Algorithms, Solutions

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Control as an Experimental Science

• Y.C. Ho, IEEE AC, Dec. 1982

• “Control” as experimental science (the 3rd dimension w.r.t. the gap)

• Experiment vs. Application (detective vs. craftsman)

• “observation-conjecture-experiment-theory-validation”

• Carried out by BOTH theorists and experimentalists

Page 28: Experimental Control Science Methodology, Algorithms, Solutions

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Experiment Discover Theorize

Page 29: Experimental Control Science Methodology, Algorithms, Solutions

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Reconnect

ControlPractice

ControlResearch

ControlTheory

Mathematics

Page 30: Experimental Control Science Methodology, Algorithms, Solutions

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The Han Paradigm

• Is it a Theory of Control or a Theory of Model?

• Paradox of Robust Control

(Godel’s Incompleteness Theorem)

• An Alternative Design Paradigm

– Explore Error-Based Control Mechanisms

– Active Disturbance Rejection

Page 31: Experimental Control Science Methodology, Algorithms, Solutions

36

Outline• IntroductionIntroduction

• QuestionsQuestions

• Research DirectionResearch Direction

• MethodologyMethodology

• Active Disturbance Rejection

• Advanced TechnologiesAdvanced Technologies

• Take AwaysTake Aways

• Open ProblemsOpen Problems

Page 32: Experimental Control Science Methodology, Algorithms, Solutions

37

Questions

• What is control & where does it belong?

• What is a good controller & how to find it?

• Does a theory-practice gap exist? Why?

• Can theoretical advance be driven by practice?

• What is the most fundamental control problem?

Page 33: Experimental Control Science Methodology, Algorithms, Solutions

38

Uncertainty principle in control?

• Kalman Filter: uncertainty of measurement

• Industry Control: uncertainty of dynamics

• Disturbance: dynamics beyond the math model

• Disturbance Uncertainty

• Control Disturbance Rejection?

Page 34: Experimental Control Science Methodology, Algorithms, Solutions

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Disturbance Rejection

• Modeling: Uncertainty ReductionExample: modeling design tuning

• Passive Disturbance RejectionExample: PID tuning

• Active Disturbance RejectionExample: Invariant Principle, ADRC (Han)

Page 35: Experimental Control Science Methodology, Algorithms, Solutions

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A Motion Control Case Study

( , , )y f y y w u

Page 36: Experimental Control Science Methodology, Algorithms, Solutions

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Model-Based Method

( , , )y f y y w u

Modeling: in analytical form

Design Goal:

Plant:

( , , )f y y w

( , )y g y y

( , , ) ( , )u f y y w g y y

Examples: pole placement; feedback linearization

Control Law:

Page 37: Experimental Control Science Methodology, Algorithms, Solutions

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Industry Practice

( , , ) ( , ) ( , )y f y y w l y y g y y

The PID example

With unknown,( , , )f y y w ( , )u l y y

( , , , ) ( )p I Dy f t y y w K e K edt K e

e r y

Page 38: Experimental Control Science Methodology, Algorithms, Solutions

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The Han Methods

• Beyond PIDNonlinear PIDTime Optimal ControlDiscrete Time Optimal ControlFind other error-based designs

• Find a way around modeling ( , , )f y y w

Page 39: Experimental Control Science Methodology, Algorithms, Solutions

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Getting around modeling

• Adding a sensor

• Estimating in real time

( , , )f y y w y u

( , , )f y y w

Page 40: Experimental Control Science Methodology, Algorithms, Solutions

45

Active Disturbance Rejection

1 2

2 3

3

1

,x xx x u

x fy x

Augmented plant in state space:

Extended State Observer (Han)

1 2 31 2 3 z x z x z x f

1 2 1 1 1

2 3 2 2 1

3 3 3 1

( )( )

( )

z z g z yz z g z y uz g z y

1 2 3, , ( , , )x y x y x f y y w

( , , ) y f y y w u

Page 41: Experimental Control Science Methodology, Algorithms, Solutions

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Active disturbance compensation

1 2

2 0

1

x xx uy x

1 2

2

1

x xx f uy x

0 3

3

u u zz f

1 2( , , )?( ) or f x x wf t

Page 42: Experimental Control Science Methodology, Algorithms, Solutions

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Observer Comparison

Luenberge Observer Extended State Observer

Plant

y(t)

w(t)

Extended

State Observer

u(t)Plant

y(t)

w(t)

Luenberger

State Observer

u(t)

yy

y y

( , , )y f y y w u

f

Page 43: Experimental Control Science Methodology, Algorithms, Solutions

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Observer Comparison

Luenberger Observer

• Needs expression of f• Model-based• For LTI systems only

Extended State Observer

• Estimates y, dy/dt, and f• Model-independent• Linear or nonlinear• TI or TV• One-parameter tuning

( , , )y f y y w u

Page 44: Experimental Control Science Methodology, Algorithms, Solutions

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( ) ( , , )ny f y y w u

0ˆu f u

( )0

ny u

Page 45: Experimental Control Science Methodology, Algorithms, Solutions

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Active Disturbance Rejection ControlADRC

• Generalized disturbance rejection:– Internal disturbance: system dynamics– External disturbance– Combined into f

• Easily tuned– Z. Gao, ACC2003

Page 46: Experimental Control Science Methodology, Algorithms, Solutions

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Bandwidth-based Tuning

0 1 2 3 4 5 60

1

2position

y z1

0 1 2 3 4 5 6-1

0

1

2velocity

dy/dtz2

0 1 2 3 4 5 6-50

0

50disturbance and unknown dyanmics

time second

f z3

0 1 2 3 4 5 60

1

2transient profile and output

bandwidth: 4 rad/sec bandwidth: 20 rad/sectransient profile

0 1 2 3 4 5 60

0.5

1error

0 1 2 3 4 5 6-1

0

1

2control signal

time second

Page 47: Experimental Control Science Methodology, Algorithms, Solutions

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Hardware Test: torque disturbance

0 2 4 6 8 10 120

0.5

1

1.5

0 2 4 6 8 10 12-0.1

0

0.1

0 2 4 6 8 10 12-5

0

5

Torque Disturbance Rejection Rev.

Rev.

Volts

Position

Position error

Control Command

ADRC

ADRC

ADRC

PID

PID

PID

Page 48: Experimental Control Science Methodology, Algorithms, Solutions

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Performance of the disturbance observer

0 1 2 3 4 5-30

-20

-10

0

10

20

30

a(t)

z3(t)

Total disturbance and its estimation

Time (sec.)

f(t)

Page 49: Experimental Control Science Methodology, Algorithms, Solutions

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Motion Control Demo

Page 50: Experimental Control Science Methodology, Algorithms, Solutions

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Outline• IntroductionIntroduction

• QuestionsQuestions

• Research DirectionResearch Direction

• MethodologyMethodology

• Active Disturbance RejectionActive Disturbance Rejection

• Advanced Technologies

• Take AwaysTake Aways

• Open ProblemsOpen Problems

Page 51: Experimental Control Science Methodology, Algorithms, Solutions

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Algorithms

• Nonlinear PID • Discrete Time Optimal Control• Active Disturbance Rejection• Single Parameter Tuning• Wavelet Controller/Filter

Page 52: Experimental Control Science Methodology, Algorithms, Solutions

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• Manufacturing (Motion, Web Tension, CNC)

• Power Electronics (Motor, PMAD, Converters)

• Aircraft (Flight, Jet Engine)

• Process Control (CSTR)

• Biomedical (Ankle)

• Health/fault Monitoring (A benchmark prob.)

• Automobile (Truck ABS)

Technologies

Page 53: Experimental Control Science Methodology, Algorithms, Solutions

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Take Aways

• Think outside “the box”

• Active disturbance rejection

• From problems to methods to methodology

http://[email protected]

Page 54: Experimental Control Science Methodology, Algorithms, Solutions

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Open Problems• Characteristics of ESO

– Convergence, – Rate of Convergence, – Boundedness– Bound of error– Order estimation– b0 estimation (Initial results)

• Practical Optimality (Initial results)• Reformulation of process control problems• Cybernetics

Page 55: Experimental Control Science Methodology, Algorithms, Solutions

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A Research Alliance• Practitioners/Researchers/Mathematicians

• Discover (both practitioners and theoreticians)

• Theorize– Prove stability and convergence– Generalize a particular solution/method– Establish a new kind of theory

• Validate – Verify the new theory against other problems– Define the range of applicability


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