Fuzzy neural friction compensation method of robot manipulation during position/force control

Kazuo Kiguchi, Toshio Fukuda

Research output: Contribution to journalConference articlepeer-review

19 Citations (Scopus)


Position and force controls are important and fundamental tasks of robot manipulators. In order to control position of the robot which simultaneously applies force to the environment, the friction between the robot and the environment has to be compensated. However, the friction force varies according to the applied force to the environment. Therefore, it is difficult to compensate the friction effectively with conventional controllers if we do not know the friction coefficient. In these days, a lot of researches have been done on fuzzy neural control, the combination of neural networks control and fuzzy control, in order to make up for each other's weak points. The fuzzy neural control is expected to perform more sophisticated control than conventional control in an unknown environment. In this paper, we propose a new friction compensation method using the fuzzy neural network which contains a specialized neuron for friction compensation and a switch-learning. Simulation has done using a 3DOF planar robot manipulator to confirm the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)372-377
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Publication statusPublished - 1996
Externally publishedYes
EventProceedings of the 1996 13th IEEE International Conference on Robotics and Automation. Part 1 (of 4) - Minneapolis, MN, USA
Duration: Apr 22 1996Apr 28 1996

All Science Journal Classification (ASJC) codes

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


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