Optimal Adaptive Control & Differential Games by Reinforcement Learning Principles

Optimal Adaptive Control & Differential Games by Reinforcement Learning Principles

eBook - 2013
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Stylus Pub
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 realtime to optimal control solutions by measuring data along the system trajectories. The book also describes how to use approximate dynamic programming methods to solve multiplayer differential games online. Differential games have been shown to be important in Hinfinity robust control for disturbance rejection, and in coordinating activities among multiple agents in networked teams. The focus of this book is on continuoustime 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.


Gardners
Shows how approximate dynamic programming 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

Ingram Publishing Services

This book gives an exposition of recently developed approximate dynamic programming (ADP) techniques for decision and control in human engineered systems.



Publisher: London : Institution of Electrical Engineers, Ă2013
ISBN: 9781849194907
1849194904
9781849194891
1849194890
Characteristics: 1 online resource (xiv, 288 pages)

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