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Functional Adaptive Control

An Intelligent Systems Approach

  • Simon G. Fabri
  • Visakan Kadirkamanathan

Part of the Communications and Control Engineering book series (CCE)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 3-19
  3. Deterministic Systems

    1. Front Matter
      Pages 21-21
    2. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 23-46
    3. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 47-78
    4. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 79-100
    5. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 101-127
  4. Stochastic Systems

    1. Front Matter
      Pages 129-129
    2. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 131-145
    3. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 147-164
    4. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 165-185
    5. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 187-212
    6. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 213-241
  5. Conclusions

    1. Front Matter
      Pages 243-243
    2. Simon G. Fabri, Visakan Kadirkamanathan
      Pages 245-249
  6. Back Matter
    Pages 251-266

About this book

Introduction

The field of intelligent control has recently emerged as a response to the challenge of controlling highly complex and uncertain nonlinear systems. It attempts to endow the controller with the key properties of adaptation, learn­ ing and autonomy. The field is still immature and there exists a wide scope for the development of new methods that enhance the key properties of in­ telligent systems and improve the performance in the face of increasingly complex or uncertain conditions. The work reported in this book represents a step in this direction. A num­ ber of original neural network-based adaptive control designs are introduced for dealing with plants characterized by unknown functions, nonlinearity, multimodal behaviour, randomness and disturbances. The proposed schemes achieve high levels of performance by enhancing the controller's capability for adaptation, stabilization, management of uncertainty, and learning. Both deterministic and stochastic plants are considered. In the deterministic case, implementation, stability and convergence is­ sues are addressed from the perspective of Lyapunov theory. When compared with other schemes, the methods presented lead to more efficient use of com­ putational storage and improved adaptation for continuous-time systems, and more global stability results with less prior knowledge in discrete-time sys­ tems.

Keywords

Adaptive Control Intelligent Control Neural Networks Nonlinear Control Simulation Stochastic Control complex systems complexity intelligence intelligent systems model uncertainty

Authors and affiliations

  • Simon G. Fabri
    • 1
  • Visakan Kadirkamanathan
    • 2
  1. 1.Department of Electrical Power and Control EngineeringUniversity of MaltaMsidaMalta
  2. 2.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldEngland

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-0319-6
  • Copyright Information Springer-Verlag London Limited 2001
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4471-1090-3
  • Online ISBN 978-1-4471-0319-6
  • Series Print ISSN 0178-5354
  • Buy this book on publisher's site