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Experimental Control Science Methodology, Algorithms, Solutions

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

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Experimental Control Science Methodology, Algorithms, Solutions

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  1. Experimental Control ScienceMethodology, Algorithms, Solutions • Zhiqiang Gao, Ph.D. • Center for Advanced Control Technologies • Cleveland State University • December 24, 2004 http://cact.csuohio.edu

  2. Outline • Introduction • Questions • Research Direction • Methodology • Active Disturbance Rejection • Advanced Technologies • Take Aways • Open Problems

  3. From Applied Research to Advanced Technologies Center for Advanced Control Technologies http://cact.csuohio.edu

  4. 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

  5. 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

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

  7. NASA Intelligent PMAD Project

  8. Web Tension Regulation

  9. Truck Anti-lock Brake System

  10. Turbofan engine

  11. 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

  12. Nonlinear 3-Tank Fault Id. Problem 6 possible faults 2 inputs 3 outputs

  13. CACT Mission • Define, Articulate, Formulate Fundamental Industrial Control Problems • Solutions and Cutting Edge Technologies • Performance and Transparency • Synergy in Research and Practice

  14. Outline • Introduction • Questions • Research Direction • Methodology • Active Disturbance Rejection • Advanced Technologies • Take Aways • Open Problems

  15. 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?

  16. How do we describe it? • An Art of Practice? • Hidden Technology? • Mathematics? • Engineering Science? • Control Science? • Natural Science?

  17. Where does control belong? • Electrical Engineering • Mechanical Engineering • Chemical Engineering • Aerospace Engineering • System Engineering • Mathematics • Biology?

  18. Is there a theory-practice gap? Control Theory ß Engineering Problem Solving ?

  19. Can theory be driven by practice? New Theory Ý ? Engineering Problem Solving

  20. Outline • Introduction • Questions • Research Direction • Methodology • Active Disturbance Rejection • Advanced Technologies • Take Aways • Open Problems

  21. Theory vs. Practice A Historical Perspective

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

  23. Classical Control Era Control Practice Control Research Mathematics Control Theory

  24. Modern Control Era Control Practice Control Research Research Mathematics unobservable uncontrollable Control Theory Theory

  25. Research: A strenuous and devoted attempt to force nature into the conceptualboxes 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. <The Structure of Scientific Revolutions> by Thomas S. Kuhn

  26. Outline • Introduction • Questions • Research Direction • Methodology • Active Disturbance Rejection • Advanced Technologies • Take Aways • Open Problems

  27. 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

  28. Experiment Discover Theorize

  29. Reconnect Control Practice Control Research Mathematics Control Theory

  30. 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

  31. Outline • Introduction • Questions • Research Direction • Methodology • Active Disturbance Rejection • Advanced Technologies • Take Aways • Open Problems

  32. 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?

  33. Uncertainty principle in control? • Kalman Filter: uncertainty of measurement • Industry Control: uncertainty of dynamics • Disturbance: dynamics beyond the math model • Disturbance Û Uncertainty • Control Û Disturbance Rejection?

  34. Disturbance Rejection • Modeling: Uncertainty Reduction Example: modeling Þ design Þ tuning • Passive Disturbance Rejection Example: PID tuning • Active Disturbance Rejection Example: Invariant Principle, ADRC (Han)

  35. A Motion Control Case Study

  36. Model-Based Method Plant: Modeling: in analytical form Design Goal: Control Law: Examples: pole placement; feedback linearization

  37. Industry Practice With unknown, The PID example

  38. The Han Methods • Beyond PID Þ Nonlinear PID Þ Time Optimal Control Þ Discrete Time Optimal Control Þ Find other error-based designs • Find a way around modeling

  39. Getting around modeling • Adding a sensor • Estimating in real time

  40. Active Disturbance Rejection Augmented plant in state space: Extended State Observer (Han)

  41. Active disturbance compensation

  42. Luenberge Observer Extended State Observer Observer Comparison

  43. 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 Observer Comparison

  44. Active Disturbance Rejection ControlADRC • Generalized disturbance rejection: • Internal disturbance: system dynamics • External disturbance • Combined into f • Easily tuned • Z. Gao, ACC2003

  45. Bandwidth-based Tuning

  46. Hardware Test: torque disturbance

  47. Performance of the disturbance observer f(t)

  48. Motion Control Demo

  49. Outline • Introduction • Questions • Research Direction • Methodology • Active Disturbance Rejection • Advanced Technologies • Take Aways • Open Problems

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