Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained.
1. 3.1 Introduction - p. 55 2. 3.2 Basic Notations and Definitions - p. 58 3. 3.3 Problem Formulation - p. 58 4. 3.4 Extremum Seeking-Based Iterative Gain Tuning for Input-Output Linearization Control - p. 59 5. 3.5 Mechatronics Examples - p. 69 6. 3.6 Conclusion and Discussion of Open Problems - p. 99 7. Chapter 4: Extremum Seeking-Based Indirect Adaptive Control - p. 100 8. Abstract - p. 101 9. 4.1 Introduction - p. 101 10. 4.2 Basic Notations and Definitions - p. 103 11. 4.3 ES-Based Indirect Adaptive Controller for the Case of General Nonlinear Models With Constant Model Uncertainties - p. 106 12. 4.4 ES-Based Indirect Adaptive Controller for General Nonlinear Models With Time-Varying Model Uncertainties - p. 109 13. 4.5 The Case of Nonlinear Models Affine in the Control - p. 111 14. 4.6 Mechatronics Examples - p. 120 15. 4.7 Conclusion - p. 137 16. Chapter 5: Extremum Seeking-Based Real-Time Parametric Identification for Nonlinear Systems - p. 140 17. Abstract - p. 141 18. 5.1 Introduction - p. 141 19. 5.2 Basic Notations and Definitions - p. 144 20. 5.3 ES-Based Open-Loop Parametric Identification for Nonlinear Systems - p. 145 21. 5.4 ES-Based Closed-Loop Parametric Identification for Nonlinear Systems - p. 149 22. 5.5 Identification and Stable PDEs′ Model Reduction by ES - p. 160 23. 5.6 Application Examples - p. 181 24. 5.7 Conclusion and Open Problems - p. 215 25. Chapter 6: Extremum Seeking-Based Iterative Learning Model Predictive Control (ESILC-MPC) - p. 221 26. Conclusions and Further Notes - p. 255 27. Index - p. 265
Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained.
1. 3.1 Introduction - p. 55 2. 3.2 Basic Notations and Definitions - p. 58 3. 3.3 Problem Formulation - p. 58 4. 3.4 Extremum Seeking-Based Iterative Gain Tuning for Input-Output Linearization Control - p. 59 5. 3.5 Mechatronics Examples - p. 69 6. 3.6 Conclusion and Discussion of Open Problems - p. 99 7. Chapter 4: Extremum Seeking-Based Indirect Adaptive Control - p. 100 8. Abstract - p. 101 9. 4.1 Introduction - p. 101 10. 4.2 Basic Notations and Definitions - p. 103 11. 4.3 ES-Based Indirect Adaptive Controller for the Case of General Nonlinear Models With Constant Model Uncertainties - p. 106 12. 4.4 ES-Based Indirect Adaptive Controller for General Nonlinear Models With Time-Varying Model Uncertainties - p. 109 13. 4.5 The Case of Nonlinear Models Affine in the Control - p. 111 14. 4.6 Mechatronics Examples - p. 120 15. 4.7 Conclusion - p. 137 16. Chapter 5: Extremum Seeking-Based Real-Time Parametric Identification for Nonlinear Systems - p. 140 17. Abstract - p. 141 18. 5.1 Introduction - p. 141 19. 5.2 Basic Notations and Definitions - p. 144 20. 5.3 ES-Based Open-Loop Parametric Identification for Nonlinear Systems - p. 145 21. 5.4 ES-Based Closed-Loop Parametric Identification for Nonlinear Systems - p. 149 22. 5.5 Identification and Stable PDEs′ Model Reduction by ES - p. 160 23. 5.6 Application Examples - p. 181 24. 5.7 Conclusion and Open Problems - p. 215 25. Chapter 6: Extremum Seeking-Based Iterative Learning Model Predictive Control (ESILC-MPC) - p. 221 26. Conclusions and Further Notes - p. 255 27. Index - p. 265
