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Iterative Learning Control with Passive Incomplete Information

Algorithms Design and Convergence Analysis

  • Dong Shen

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Dong Shen
    Pages 1-20
  3. One-Side Data Dropout

  4. Two-Side Data Dropout

    1. Front Matter
      Pages 161-161
  5. General Incomplete Information Conditions

  6. Back Matter
    Pages 287-294

About this book

Introduction

This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC.

It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation algorithms for the incomplete data, namely, the intermittent update algorithm and successive update algorithm. Incomplete information environments include random data dropout, random communication delay, random iteration-varying lengths, and other communication constraints.

With numerous intuitive figures to make the content more accessible, the book explores several potential solutions to this topic, ensuring that readers are not only introduced to the latest advances in ILC for systems with random factors, but also gain an in-depth understanding of the intrinsic relationship between incomplete information environments and essential tracking performance. It is a valuable resource for academics and engineers, as well as graduate students who are interested in learning about control, data-driven control, networked control systems, and related fields.

Keywords

Iterative Learning Control ILC Incomplete Information ilc partial information Algorithm Design Convergence Analysis Data Dropouts Communication Constraints Algorithm Asynchronization Almost Sure Convergence Mean Square Convergence Tracking performance of ILC stochastic ILC

Authors and affiliations

  • Dong Shen
    • 1
  1. 1.College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijingChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-8267-2
  • Copyright Information Springer Nature Singapore Pte Ltd. 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Engineering
  • Print ISBN 978-981-10-8266-5
  • Online ISBN 978-981-10-8267-2
  • Buy this book on publisher's site