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Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

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IET Digital Library

This title is available electronically through the IET Digital Library

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  • Author:

  • Year: 2012

  • Format: Hardback

  • Product Code: PBCE0810

  • ISBN: 978-1-84919-489-1

  • Pagination: 400 pp.

  • Stock Status: In stock

£64.35 Member price

£99.00 Full price


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Description

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.

Book readership

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.

Book contents

  • Introduction to optimal control, adaptive control and reinforcement learning
  • Reinforcement learning and optimal control of discrete-time systems: Using natural decision methods to design optimal adaptive controllers
  • Optimal adaptive control using integral reinforcement learning for linear systems
  • Integral reinforcement learning (IRL) for non-linear continuous-time systems
  • Generalized policy iteration for continuous-time systems
  • Value iteration for continuous-time systems
  • Optimal adaptive control using synchronous online learning
  • Synchronous online learning with integral reinforcement
  • Synchronous online learning for zero-sum two-player games and H-infinity control
  • Synchronous online learning for multiplayer non–zero-sum games
  • Integral reinforcement learning for zero-sum two-player games
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