標題: On-line genetic fuzzy-neural sliding mode controller design
作者: Lin, PZ
Wang, WY
Lee, TT
Chen, GM
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: BMF fuzzy-neural sliding mode controllers;online adaptive bound reduced-form genetic algorithms;robot manipulators
公開日期: 2005
摘要: In this paper, a novel online B-spline membership function (BMF) fuzzy-neural sliding mode controller trained by an adaptive bound reduced-form genetic algorithm (ABRGA) is developed to guarantee robust stability and tracking performance for robot manipulators with uncertainties and external disturbances. The general sliding manifold is used to construct the sliding surface and reduce the chattering of the control signal, which can be nonlinear or time varying. For the purpose of identification, the proposed BMF fuzzy-neural network trained by the ABRGA approximates the regressor of the manipulator. An adaptive bound algorithm is used to aid and speed up the searching speed of the RGA. Simulation results show that the proposed on-line ABRGA-based BMF fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.
URI: http://hdl.handle.net/11536/17585
ISBN: 0-7803-9298-1
ISSN: 1062-922X
期刊: INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOL 1-4, PROCEEDINGS
起始頁: 245
結束頁: 250
Appears in Collections:Conferences Paper