Synopsis
Learning-based Adaptive Control: An Extremum Seeking Approach - Theory and Applications presents comprehensive information on Adaptive Control for optimal action based on the current characteristics of a system, also presenting tactics on how to learn how characteristics change along the way. The book takes two main approaches to the learning process: Model Based and Model Free. In the first case, precision modeling of the characteristics of a system and stability are key. For Model Free Adaptive Control, you begin knowing nothing about the system and learn from action (inputs) and reaction (outputs). This book offers a blended approach to Adaptive Control through physical modeling that allows the characteristics to evolve naturally over time to maintain the stability of the system. In the cases presented by the author, there is remarkable gain in performance and adaptability of the systems by applying his findings.
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About the Author
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.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Englisch. Seller Inventory # 9780128031360
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9780128031360