Iterative Learning Control

An Optimization Paradigm

  • David H.¬†Owens

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

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. David H. Owens
    Pages 1-17
  3. David H. Owens
    Pages 19-54
  4. David H. Owens
    Pages 55-86
  5. David H. Owens
    Pages 119-144
  6. David H. Owens
    Pages 145-163
  7. David H. Owens
    Pages 165-207
  8. David H. Owens
    Pages 209-231
  9. David H. Owens
    Pages 233-276
  10. David H. Owens
    Pages 277-321
  11. David H. Owens
    Pages 323-346
  12. David H. Owens
    Pages 347-375
  13. David H. Owens
    Pages 377-402
  14. David H. Owens
    Pages 403-443
  15. Back Matter
    Pages 445-456

About this book

Introduction

This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities.

Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other electromechanical and/or mechanical systems.

Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.


Keywords

Control Applications Control Engineering Control Theory Iterative Learning Control Parameter Optimization Signal Optimization

Authors and affiliations

  • David H.¬†Owens
    • 1
  1. 1.University of SheffieldSheffieldUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-6772-3
  • Copyright Information Springer-Verlag London 2016
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-4471-6770-9
  • Online ISBN 978-1-4471-6772-3
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • About this book