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" Iterative Learning Control for Deterministic Systems "
by Kevin L. Moore.
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BL
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621492
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dltt
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| Main Entry
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Moore, Kevin L.
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| Title & Author
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Iterative Learning Control for Deterministic Systems\ by Kevin L. Moore.
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| Publication Statement
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London :: Springer London,, 1993.
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| Series Statement
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Advances in Industrial Control,
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| ISBN
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9781447119128
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: 9781447119142
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| Contents
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Contents: Learning Control: An Overview -- Linear Time-Invariant Learning Control -- LTI Learning Control via Parameter Estimation -- Finite-Horizon Learning Control -- Learning Control for Nonlinear Systems -- Time-Varying Learning Controller for a Class of Nonlinear Systems -- Artificial Neural Networks for Nonlinear Learning Control -- Appendix A: Basic Results on Multirate Sampling -- Appendix B: Neural Networks: An Overview.
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| Abstract
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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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Engineering.
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Computer-aided design.
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Engineering economy.
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Machinery.
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SpringerLink (Online service)
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