Neural network-based optimal adaptive tracking using genetic algorithms

Sisil Kumarawadu, Keigo Watanabe, Kiyotaka Izumi, Kazuo Kiguchi

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This paper presents the use of neural networks (NNs) and genetic algorithms (GAs) to enhance the output tracking performance of partly known robotic systems. Two of the most potential approaches of adaptive control, i.e., the concept of variable structure control (VSC) and NN-based adaptive control, are ingeniously combined using GAs to achieve high-performance output tracking. GA is used to make the maximum use of different performance characteristics of two self-adaptive NN modules by finding the switching function which best combines them. The method will be valid for any rigid revolute robot system. Computer simulations on our active binocular head are included for illustration and verification.

Original languageEnglish
Pages (from-to)372-384
Number of pages13
JournalAsian Journal of Control
Volume8
Issue number4
Publication statusPublished - Dec 1 2006
Externally publishedYes

Keywords

  • Gaussian-sum networks
  • Genetic algorithms
  • Neural networks
  • Robot control
  • Softmax function

ASJC Scopus subject areas

  • Control and Systems Engineering

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