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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.
- Includes a good number of Mechatronics Examples of the techniques.
- Compares and blends Model-free and Model-based learning algorithms.
- Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.
Researchers and graduate students in adaptive robust control. Engineers in the mechatronics field
Chapter 1: Some Mathematical Tools
- 1.1 Norms Definitions and Properties
- 1.2 Vector Functions and Their Properties
- 1.3 Stability of Dynamical Systems
- 1.4 Dynamical Systems Affine in the Control
- 1.5 Geometric, Topological, and Invariance Set Properties
- 1.6 Conclusion
Chapter 2: Adaptive Control: An Overview
- 2.1 Introduction
- 2.2 Adaptive Control Problem Formulation
- 2.3 Model-Based Adaptive Control
- 2.4 Model-Free Adaptive Control
- 2.5 Learning-Based Adaptive Control
- 2.6 Conclusion
Chapter 3: Extremum Seeking-Based Iterative Feedback Gains Tuning Theory
- 3.1 Introduction
- 3.2 Basic Notations and Definitions
- 3.3 Problem Formulation
- 3.4 Extremum Seeking-Based Iterative Gain Tuning for Input-Output Linearization Control
- 3.5 Mechatronics Examples
- 3.6 Conclusion and Discussion of Open Problems
Chapter 4: Extremum Seeking-Based Indirect Adaptive Control
- 4.1 Introduction
- 4.2 Basic Notations and Definitions
- 4.3 ES-Based Indirect Adaptive Controller for the Case of General Nonlinear Models With Constant Model Uncertainties
- 4.4 ES-Based Indirect Adaptive Controller for General Nonlinear Models With Time-Varying Model Uncertainties
- 4.5 The Case of Nonlinear Models Affine in the Control
- 4.6 Mechatronics Examples
- 4.7 Conclusion
Chapter 5: Extremum Seeking-Based Real-Time Parametric Identification for Nonlinear Systems
- 5.1 Introduction
- 5.2 Basic Notations and Definitions
- 5.3 ES-Based Open-Loop Parametric Identification for Nonlinear Systems
- 5.4 ES-Based Closed-Loop Parametric Identification for Nonlinear Systems
- 5.5 Identification and Stable PDEs′ Model Reduction by ES
- 5.6 Application Examples
- 5.7 Conclusion and Open Problems
Chapter 6: Extremum Seeking-Based Iterative Learning Model Predictive Control (ESILC-MPC)
- 6.1 Introduction
- 6.2 Notation and Basic Definitions
- 6.3 Problem Formulation
- 6.4 The DIRECT ES-Based Iterative Learning MPC
- 6.5 Dither MES-Based Adaptive MPC
- 6.6 Numerical Examples
- 6.7 Conclusion and Open Problems
- No. of pages:
- © Butterworth-Heinemann 2016
- 11th July 2016
- Paperback ISBN:
- eBook ISBN:
Mouhacine Benosman worked at universities in Rome, Italy, Reims, France, and Glasgow, Scotland before spending 5 years as a Research Scientist with the Temasek Laboratories at the National University of Singapore.
He is presently senior researcher at the Mitsubishi Electric Research Laboratories (MERL), Cambridge, USA. His research interests include modelling and control of flexible systems, non-linear robust and fault tolerant control, vibration suppression in industrial machines, multi-agent control with applications to smart-grid, and more recently his research focus is on learning and adaptive control with application to mechatronics systems.
The author has published more than 40 peer-reviewed journals and conferences, and more than 10 patents in the field of mechatronics systems control. He is a senior member of the IEEE society and an Associate Editor of the Control System Society Conference Editorial Board.
Senior Researcher, Mitsubishi Electric Research Laboratories (MERL), Cambridge, USA
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