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.
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.