Adaptive controllers and optimal controllers are two distinct methods for the design of automatic control systems. Adaptive controllers learn online in real time how to control systems but do not yield optimal performance, whereas optimal controllers must be designed offline using full knowledge of the systems dynamics. This book shows how approximate dynamic programming – a reinforcement machine learning technique that is motivated by learning mechanisms in biological and animal systems - can be used to design a family of adaptive optimal control algorithms that converge in real-time to optimal control solutions by measuring data along the system trajectories.
The book also describes how to use approximate dynamic programming methods to solve multi-player differential games online. Differential games have been shown to be important in H-infinity robust control for disturbance rejection, and in coordinating activities among multiple agents in networked teams.
The focus of this book is on continuous-time systems, whose dynamical models can be derived directly from physical principles based on Hamiltonian or Lagrangian dynamics. Simulation examples are given throughout the book, and several methods are described that do not require full state dynamics information.
Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles is an essential addition to the bookshelves of mechanical, electrical, and aerospace engineers working in feedback control systems design.