Iterative Learning Control for Deterministic Systems

Name: Iterative Learning Control for Deterministic Systems
Brand: Springer London Ltd
SKU: 9781447119142
Price: 19274 HUF
Availability: InStock

Szerző: Kevin L. Moore

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer London Ltd

Elérhetőség: Beszállítói készleten

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Iterative Learning Control for Deterministic Systems is part of the new Advances in Industrial Contr...

Információk a könyvről

Szerző

Kevin L. Moore

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2011

oldal

152

EAN

9781447119142

ISBN

1447119142

Enbook ID

02179415

Kiadó

Springer London Ltd

Súly

272

Méretek

155 x 235 x 10

Teljes leírás

Iterative Learning Control for Deterministic Systems is part of the new Advances in Industrial Control series, edited by Professor M.J. Grimble and Dr. M.A. Johnson of the Industrial Control Unit, University of Strathclyde. The material presented in this book addresses the analysis and design of learning control systems. It begins with an introduction to the concept of learning control, including a comprehensive literature review. The text follows with a complete and unifying analysis of the learning control problem for linear LTI systems using a system-theoretic approach which offers insight into the nature of the solution of the learning control problem. Additionally, several design methods are given for LTI learning control, incorporating a technique based on parameter estimation and a one-step learning control algorithm for finite-horizon problems. Further chapters focus upon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems, including the models of typical robotic manipulators. The book concludes with the application of artificial neural networks to the learning control problem. Three specific ways to neural nets for this purpose are discussed, including two methods which use backpropagation training and reinforcement learning. The appendices in the book are particularly useful because they serve as a tutorial on artificial neural networks.

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Iterative Learning Control for Deterministic Systems

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