Robust self-tuning rotated fuzzy basis function controller for robot arms

Author

Lin, C.-K. ; Wang, S.-D.

Author_Institution

Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan

Volume

144

Issue

4

fYear

1997

fDate

7/1/1997 12:00:00 AM

Firstpage

293

Lastpage

298

Abstract

An adaptive fuzzy controller is developed for a serial-link robot arm. The proposed rotated fuzzy basis function (RFBF) controller is a more flexible fuzzy basis function expansion to approximate unknown functions of the robot model. All parameters of RFBF neural network can be tuned online when the number of rules is determined. In the control design, the unmodelled dynamics are considered. Moreover, the stability analysis shows that the states and tracking errors of the robot arm are uniformly bounded. Simulations of the proposed controller on the PUMA-560 robot arm demonstrate the effectiveness

Keywords

adaptive control; fuzzy control; fuzzy neural nets; manipulators; neurocontrollers; robust control; self-adjusting systems; PUMA-560 robot arm; RFBF neural network; adaptive fuzzy controller; control design; fuzzy basis function expansion; online parameter tuning; robust self-tuning rotated fuzzy basis function controller; serial-link robot arm; stability analysis; uniformly bounded states; uniformly bounded tracking errors; unknown function approximation;

fLanguage

English

Journal_Title

Control Theory and Applications, IEE Proceedings -

Publisher

iet

ISSN

1350-2379

Type

jour

DOI

10.1049/ip-cta:19971000

Filename

610220