Generation of efficient adjustment strategies for a fuzzy-neuro force controller using genetic algorithms - Application to robot force control in an unknown environment

Kazuo Kiguchi, Keigo Watanabe, Toshio Fukuda

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

This paper presents an effective generation method of adjustment strategies for a fuzzy-neuro force controller (FNFC) of a robot manipulator in an unknown environment. In this method, strategies to adjust the FNFC in accordance with the environment dynamics are automatically generated in off-line manner using genetic algorithms (GA). The generated strategies are stored in a neural network and used for adjusting the FNFC in on-line. Therefore, the FNFC is automatically adjusted in accordance with the unknown dynamics of an environment using the generated strategies which are stored in the neural network. Fuzzy fitness evaluation method is proposed for the effective evolution of the neural network in the GA process. The effectiveness of the generated adjustment strategies of the FNFC has been evaluated by computer simulation with a 3DOF robot manipulator model.

Original languageEnglish
Pages (from-to)113-126
Number of pages14
JournalInformation Sciences
Volume145
Issue number1-2
DOIs
Publication statusPublished - Aug 1 2002
Externally publishedYes

Keywords

  • Force control
  • Robot manipulator
  • Soft computing
  • Unknown environment

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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