Selection of optimal learning rates in CMAC based control schemes

Wen Chi Luo*, Kai-Tai Song

*Corresponding author for this work

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

CMAC based control schemes have been studied by many researchers. It is well recognized that properly designed CMAC controllers provide useful and practical tools for precision control of non-linear systems. For complex trajectories, however, the convergence speed of CMAC can be slow because the CMAC module takes much time in learning the inverse dynamics of the plant. Therefore, one practical difficulty of CMAC based controller design is the selection of appropriate learning rate. In this paper, we present a method for selection of optimal CMAC learning rate. Furthermore, we demonstrate that the proposed GA-based approach to parameter selection can provide a global optimal solution. Computer simulation results confirm the effectiveness of the proposed method.

Original languageEnglish
Pages212-216
Number of pages5
StatePublished - 1 Dec 2002
EventProceedings of the 2002 IEEE International Symposium on Intelligent Control - Vancouver, Canada
Duration: 27 Oct 200230 Oct 2002

Conference

ConferenceProceedings of the 2002 IEEE International Symposium on Intelligent Control
CountryCanada
CityVancouver
Period27/10/0230/10/02

Keywords

  • Artificial neural networks
  • Genetic algorithms
  • Learning control
  • Parameter optimization

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    Luo, W. C., & Song, K-T. (2002). Selection of optimal learning rates in CMAC based control schemes. 212-216. Paper presented at Proceedings of the 2002 IEEE International Symposium on Intelligent Control, Vancouver, Canada.