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Iterative Learning Control for Deterministic Systems

  • Book
  • © 1993

Overview

Authors:
  1. Kevin L. Moore

    1. College of Engineering, Idaho State University, Pocatello, USA

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Part of the book series: Advances in Industrial Control (AIC)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Introduction to the Monograph

    • Kevin L. Moore
    Pages 1-7

  3. Iterative Learning Control: An Overview

    • Kevin L. Moore
    Pages 9-22

  4. Linear Time-Invariant Learning Control

    • Kevin L. Moore
    Pages 23-35

  5. LTI Learning Control via Parameter Estimation

    • Kevin L. Moore
    Pages 37-44

  6. Finite-Horizon Learning Control

    • Kevin L. Moore
    Pages 45-61

  7. Nonlinear Learning Control

    • Kevin L. Moore
    Pages 63-77

  8. Artificial Neural Networks for Iterative Learning Control

    • Kevin L. Moore
    Pages 79-97

  9. Conclusion

    • Kevin L. Moore
    Pages 99-101

  10. Back Matter

    Pages 103-152
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Keywords

About this book

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 specificways 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.

Authors and Affiliations

  • College of Engineering, Idaho State University, Pocatello, USA

    Kevin L. Moore

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