Neural network controllers for robot manipulators application of damping neurons

Kazuo Kiguchi, Toshio Fukuda

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


_This paper presents an effective adaptive neural network feedback controller for force control of robot manipulators in an unknown environment by applying damping neurons which possess elastic-viscous properties. The unexpected overshooting and oscillation caused by theunknown and/or unmodeled dynamics of a robot manipulator and an environment can be decreased efficiently by the effect of the proposed damping neurons. Furthermore, a fuzzy controlled evaluation function is applied for the learning of the proposed neural network controller, so that the controlleris able to adapt to the unknown environment more effectively. The effectiveness of the proposed neural network controller is evaluated by experiment with a 3 d.o.f. direct-drive planar robot manipulator.

Original languageEnglish
Pages (from-to)191-208
Number of pages18
JournalAdvanced Robotics
Issue number3
Publication statusPublished - Jan 1 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications


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