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Strategies for Feedback Linearisation

A Dynamic Neural Network Approach

  • Freddy Garces
  • Victor M. Becerra
  • Chandrasekhar Kambhampati
  • Kevin Warwick

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

Table of contents

  1. Front Matter
    Pages i-xv
  2. Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick
    Pages 1-13
  3. Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick
    Pages 15-26
  4. Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick
    Pages 27-60
  5. Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick
    Pages 61-99
  6. Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick
    Pages 101-119
  7. Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick
    Pages 121-133
  8. Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati, Kevin Warwick
    Pages 135-160
  9. Back Matter
    Pages 161-171

About this book

Introduction

The series Advances in Industrial Control aims to report and encourage of control technology transfer in control engineering. The rapid development technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Nonlinear control methods continue to exert a continuing fascination for current researchers in control systems techniques. Many industrial systems are nonlinear as was so ably demonstrated in the recent Advances in Industrial Control monograph on hydraulic servo-systems by M. Jelali and A. Kroll. However, the need to use a nonlinear control technique depends on the severity of the nonlinearity and the performance specification of the application. In some cases it is imperative that a nonlinear technique be used. The type of technique which is applied usually depends on the available information on the system description. This is the key determinant in the development of new nonlinear control methods. Over the next few years it is hoped that the nonlinear control paradigm will produce several methods which will be easily and widely applicable in industrial problems. In the meantime the search and development research go on.

Keywords

Extension control control theory feedback feedback control identification linearisation mimo system neural network neural networks non-linear identification nonlinear control nonlinear system system system identification

Authors and affiliations

  • Freddy Garces
    • 1
  • Victor M. Becerra
    • 1
  • Chandrasekhar Kambhampati
    • 2
  • Kevin Warwick
    • 3
  1. 1.Department of CyberneticsUniversity of ReadingUK
  2. 2.Department of Computer ScienceThe University of HullUK
  3. 3.Department of CyberneticsUniversity of ReadingUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-0065-2
  • Copyright Information Springer-Verlag London 2003
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4471-1095-8
  • Online ISBN 978-1-4471-0065-2
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • Buy this book on publisher's site
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