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Decentralized Neural Control: Application to Robotics

  • Ramon Garcia-Hernandez
  • Michel Lopez-Franco
  • Edgar N. Sanchez
  • Alma y. Alanis
  • Jose A. Ruz-Hernandez

Part of the Studies in Systems, Decision and Control book series (SSDC, volume 96)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez
    Pages 1-7
  3. Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez
    Pages 9-18
  4. Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez
    Pages 19-32
  5. Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez
    Pages 33-43
  6. Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez
    Pages 45-54
  7. Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez
    Pages 55-68
  8. Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez
    Pages 69-109
  9. Ramon Garcia-Hernandez, Michel Lopez-Franco, Edgar N. Sanchez, Alma Y. Alanis, Jose A. Ruz-Hernandez
    Pages 111-111

About this book

Introduction

This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors.

This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF).

The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold.

The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network.

The third control scheme applies a decentralized neural inverse optimal control for stabilization.

The fourth decentralized neural inverse optimal control is designed for trajectory tracking.

This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work. 

Keywords

Decentralized Neural Control Robotics Computational Intelligence Intelligent Systems Neural Control

Authors and affiliations

  • Ramon Garcia-Hernandez
    • 1
  • Michel Lopez-Franco
    • 2
  • Edgar N. Sanchez
    • 3
  • Alma y. Alanis
    • 4
  • Jose A. Ruz-Hernandez
    • 5
  1. 1.Universidad Autonoma del Carmen Cd. del CarmenMexico
  2. 2.ZapopanMexico
  3. 3.ZapopanMexico
  4. 4.Universidad de Guadalajara GuadalajaraMexico
  5. 5.Universidad Autonoma del Carmen Cd. del CarmenMexico

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-53312-4
  • Copyright Information Springer International Publishing Switzerland 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-53311-7
  • Online ISBN 978-3-319-53312-4
  • Series Print ISSN 2198-4182
  • Series Online ISSN 2198-4190
  • Buy this book on publisher's site
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