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Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles (Hardcover)
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Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles (Hardcover)
Series: Control, Robotics and Sensors
Expected to ship within 10 - 15 working days
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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."
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