© 1993

Iterative Learning Control for Deterministic Systems


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

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Kevin L. Moore
    Pages 1-7
  3. Kevin L. Moore
    Pages 9-22
  4. Kevin L. Moore
    Pages 23-35
  5. Kevin L. Moore
    Pages 45-61
  6. Kevin L. Moore
    Pages 63-77
  7. Kevin L. Moore
    Pages 99-101
  8. Back Matter
    Pages 103-152

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 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.


Extension algorithms artificial neural network artificial neural networks associative memory backpropagation control control system intelligent control learning neural networks nonlinear system optimization reinforcement learning robot

Authors and affiliations

  1. 1.College of EngineeringIdaho State UniversityPocatelloUSA

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