Robot manipulator contact force control application of fuzzy-neural network

Kazuo Kiguchi, Toshio Fukuda

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)


A lot of researches have been done on 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, in order to make up for each other's weak points. In this paper, fuzzy-neural controller is introduced for robot manipulator contact force control to an unknown environment. A robot manipulator controller, which approaches, contacts and applies force to the environment, 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 in order to achieve desired contact force control. Error between desired force and measured force and momentum of robot manipulator are used as input signals of the controller. Simulation has done using a 3DOF planar robot manipulator to confirm the effectiveness of the controller for the tasks of approach, contact and force control.

Original languageEnglish
Pages (from-to)875-880
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of 3) - Nagoya, Jpn
Duration: May 21 1995May 27 1995

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Control and Systems Engineering


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