Self-learning optimal control of nonlinear systems. Adaptive dynamic programming approach (Q1635523)

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.