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Intelligent Optimal Design of CMAC Neural Network for Robot Manipulators

  • Young H. Kim
  • Frank L. Lewis
Part of the Studies in Fuzziness and Soft Computing book series (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.

Keywords

Lyapunov Function Tracking Error Soft Computing Robot Manipulator Robotic Manipulator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Young H. Kim
    • 1
  • Frank L. Lewis
    • 1
  1. 1.Automation and Robotics Research InstituteThe University of Texas at ArlingtonFort WorthUSA

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