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Advancing Control Technologies: Methodologies and Challenges in Experimental Science

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This document outlines the research and methodologies utilized by the Center for Advanced Control Technologies at Cleveland State University, directed by Dr. Zhiqiang Gao. It explores critical questions in control science, identifies open problems, and discusses the importance of active disturbance rejection techniques. The combined expertise of faculty across multiple engineering disciplines offers insights into performance transparency and cutting-edge solutions derived from past projects. Key sponsors include NASA and Rockwell Automation, reflecting strong ties between theoretical advancements and practical applications.

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Advancing Control Technologies: Methodologies and Challenges in Experimental Science

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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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