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Soft Computing for Intelligent Robotic Systems pp 113–140Cite as

Intelligent Optimal Design of CMAC Neural Network for Robot Manipulators

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 21))

Abstract

This chapter presents the application of quadratic optimization for motion control to feedback control of robotic systems using Cerebellar Model Arithmetic Computer (CMAC) neural networks. Explicit solutions to the Hamilton-Jacobi-Bellman (H-J-B) equation for optimal control of robotic systems are found by solving an algebraic Riccati equation. It is shown how CMAC can cope with nonlinearities through optimization with no preliminary off-line learning phase required. The adaptive learning algorithm is derived from Lyapunov stability analysis, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system. The filtered tracking error or critic gain and the Lyapunov function for the nonlinear analysis are derived from the user input in terms of a specified quadratic performance index. Simulation results on a two-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances.

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Author information

Authors and Affiliations

  1. Automation and Robotics Research Institute, The University of Texas at Arlington, Fort Worth, TX, 76118, USA

    Young H. Kim & Frank L. Lewis

Authors
  1. Young H. Kim
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  2. Frank L. Lewis
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Editor information

Editors and Affiliations

  1. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Adelaide, Mawson Lakes, S.A., Australia

    Lakhmi C. Jain

  2. Center for Cooperative Research in Advanced Science and Technology, Nagoya University, Furo-cho, Chikusa-ku, 464-01, Nagoya, Japan

    Toshio Fukuda

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© 1998 Springer-Verlag Berlin Heidelberg

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Kim, Y.H., Lewis, F.L. (1998). Intelligent Optimal Design of CMAC Neural Network for Robot Manipulators. In: Jain, L.C., Fukuda, T. (eds) Soft Computing for Intelligent Robotic Systems. Studies in Fuzziness and Soft Computing, vol 21. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1882-6_5

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  • DOI: https://doi.org/10.1007/978-3-7908-1882-6_5

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-13003-2

  • Online ISBN: 978-3-7908-1882-6

  • eBook Packages: Springer Book Archive

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