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.