Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

Design, Analysis and Matlab Simulation

  • Jinkun┬áLiu

Table of contents

  1. Front Matter
    Pages i-xv
  2. Jinkun Liu
    Pages 1-17
  3. Jinkun Liu
    Pages 71-112
  4. Jinkun Liu
    Pages 113-132
  5. Jinkun Liu
    Pages 251-292
  6. Jinkun Liu
    Pages 293-309
  7. Jinkun Liu
    Pages 311-337
  8. Back Matter
    Pages 363-365

About this book


Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.
This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation.

Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.


MATLAB Simulation Mechanical Systems Neural Adaptive Control Neural Network RBF (Radial Basis Function)

Authors and affiliations

  • Jinkun┬áLiu
    • 1
  1. 1.School of Automation Sci & Electr. Eng.Beihang UniversityBeijingChina, People's Republic

Bibliographic information

  • DOI
  • Copyright Information Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-34815-0
  • Online ISBN 978-3-642-34816-7
  • Buy this book on publisher's site
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