On-line adaptive control of robot manipulators using dynamic fuzzy neural networks

Yang Gao, M.J. Er, W.E. Leithead, D.J. Leith

Research output: Contribution to conferencePaper

6 Citations (Scopus)

Abstract

This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for motion control of a multilink robot manipulator. The proposed controller has the following salient features: (1) dynamic fuzzy neural networks structure, i.e. fuzzy control rules can be generated or deleted automatically, (2) adaptive learning, (3) online learning of the robot dynamics, (4) fast learning speed, and (5) fast convergence of tracking error. Global stability of the system is established using Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that excellent tracking performance can be achieved under external disturbances
Original languageEnglish
Pages4828-4833
Number of pages5
Publication statusPublished - 27 Jun 2001
EventAmerican Control Conference - Arlington, VA, USA
Duration: 25 Jun 200127 Jun 2001

Conference

ConferenceAmerican Control Conference
CityArlington, VA, USA
Period25/06/0127/06/01

Keywords

  • on-line
  • adaptive control
  • robot manipulators
  • dynamic
  • fuzzy neural networks

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  • Cite this

    Gao, Y., Er, M. J., Leithead, W. E., & Leith, D. J. (2001). On-line adaptive control of robot manipulators using dynamic fuzzy neural networks. 4828-4833. Paper presented at American Control Conference, Arlington, VA, USA, .