Learning-Based Adaptive Control. An Extremum Seeking Approach - Theory and Applications

✍ Scribed by Mouhacine Benosman

• Publisher: Butterworth-Heinemann
• Year: 2016
• Tongue: English
• Leaves: 275
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Content:
Front Matter,Copyright,Preface,AcknowledgmentsEntitled to full textChapter 1 - Some Mathematical Tools, Pages 1-17
Chapter 2 - Adaptive Control: An Overview, Pages 19-53
Chapter 3 - Extremum Seeking-Based Iterative Feedback Gains Tuning Theory, Pages 55-100
Chapter 4 - Extremum Seeking-Based Indirect Adaptive Control, Pages 101-140
Chapter 5 - Extremum Seeking-Based Real-Time Parametric Identification for Nonlinear Systems, Pages 141-221
Chapter 6 - Extremum Seeking-Based Iterative Learning Model Predictive Control (ESILC-MPC), Pages 223-254
Conclusions and Further Notes, Pages 255-264
Index, Pages 265-270

✦ Subjects

Adaptive control systems;TECHNOLOGY & ENGINEERING;Engineering (General)