Self-learning optimal control of nonlinear systems. Adaptive dynamic programming approach. (English) Zbl 1403.49002
Studies in Systems, Decision and Control 103. Singapore: Springer; Beijing: Science Press (ISBN 978-981-10-4079-5/hbk; 978-7-03-052060-9/hbk;978-981-10-4080-1/ebook). xviii, 230 p. (2018).
In this book, based on Adaptive Dynamic Programming (ADP) techniques, which quantitatively obtain the optimal control schemes of the systems, a class of novel, self-learning, optimal control schemes is investigated.
The book contains ten chapters. The first chapter includes the basic principles for ADP algorithms. Chapter 2 proposes a finite horizon iterative ADP algorithm in order to solve the optimal control problem associated with a class of discrete-time nonlinear systems. The next three chapters develop Q-learning algorithms. Chapters 6 and 7 investigate discrete-time nonlinear systems with general multi-objective performance index functions. Chapter 8 is focused on continuous-time chaotic systems and in Chapter 9 estimates the optimal tracking control of unknown chaotic systems. Finally, Chapter 10 develops a new ADP-based sensor scheduling scheme.
The present book contains various real-world examples to illustrate the developed mathematical analysis. Thus, it is a valuable and important guide for engineers, researchers, and students in systems, decision and control science.
The book contains ten chapters. The first chapter includes the basic principles for ADP algorithms. Chapter 2 proposes a finite horizon iterative ADP algorithm in order to solve the optimal control problem associated with a class of discrete-time nonlinear systems. The next three chapters develop Q-learning algorithms. Chapters 6 and 7 investigate discrete-time nonlinear systems with general multi-objective performance index functions. Chapter 8 is focused on continuous-time chaotic systems and in Chapter 9 estimates the optimal tracking control of unknown chaotic systems. Finally, Chapter 10 develops a new ADP-based sensor scheduling scheme.
The present book contains various real-world examples to illustrate the developed mathematical analysis. Thus, it is a valuable and important guide for engineers, researchers, and students in systems, decision and control science.
Reviewer: Savin Treanta (Bucharest)
MSC:
49-02 | Research exposition (monographs, survey articles) pertaining to calculus of variations and optimal control |
49L20 | Dynamic programming in optimal control and differential games |
90C39 | Dynamic programming |
49N90 | Applications of optimal control and differential games |
68T05 | Learning and adaptive systems in artificial intelligence |
93C55 | Discrete-time control/observation systems |
90C29 | Multi-objective and goal programming |
34H10 | Chaos control for problems involving ordinary differential equations |