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Table of contents

  1. Front Matter
  2. Pages 1-14
  3. Pages 15-32
  4. Pages 33-40
  5. Pages 81-114
  6. Back Matter

About this book

Introduction

Control of Flexible-link Manipulators Using Neural Networks addresses the difficulties that arise in controlling the end-point of a manipulator that has a significant amount of structural flexibility in its links. The non-minimum phase characteristic, coupling effects, nonlinearities, parameter variations and unmodeled dynamics in such a manipulator all contribute to these difficulties. Control strategies that ignore these uncertainties and nonlinearities generally fail to provide satisfactory closed-loop performance. This monograph develops and experimentally evaluates several intelligent (neural network based) control techniques to address the problem of controlling the end-point of flexible-link manipulators in the presence of all the aforementioned difficulties. To highlight the main issues, a very flexible-link manipulator whose hub exhibits a considerable amount of friction is considered for the experimental work. Four different neural network schemes are proposed and implemented on the experimental test-bed. The neural networks are trained and employed as online controllers.

Keywords

Flexible-link manipulators Robotics control control theory mechatronics neural networks

Bibliographic information

  • DOI https://doi.org/10.1007/BFb0110411
  • Copyright Information Springer-Verlag London Limited 2001
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-85233-409-3
  • Online ISBN 978-1-84628-572-1
  • Series Print ISSN 0170-8643
  • Series Online ISSN 1610-7411
  • Buy this book on publisher's site