Fuzzy neural controller for robot manipulator force control

Kazuo Kiguchi, Toshio Fukuda

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)

Abstract

In these days, fuzzy-neural control, the combination of neural networks control which has a learning ability from experiments and fuzzy control which has an ability of dealing with human knowledge, has been studied in order to make up for each other's weak points. In this paper, fuzzy-neural controller is introduced for robot manipulator force control to an unknown environment. A robot manipulator controller for force control is designed using fuzzy logic in order to realize human like control and then modeled as a neural network to adjust membership functions and rules to achieve desired force control. As a new method, an error between desired force and measured force and momentum or robot manipulator are used as input signals of the controller. Simulation has done to confirm the effectiveness of the controller.

Original languageEnglish
Pages869-874
Number of pages6
Publication statusPublished - Jan 1 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5) - Yokohama, Jpn
Duration: Mar 20 1995Mar 24 1995

Other

OtherProceedings of the 1995 IEEE International Conference on Fuzzy Systems. Part 1 (of 5)
CityYokohama, Jpn
Period3/20/953/24/95

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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