Browsing by Author "Chen, GM"

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Adaptive fuzzy control with PI learning algorithm for induction servomotor systems
(2005)
Chen, GM
;
Hsu, CF
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes an adaptive fuzzy controller (AFC) with a proportional-integral (PI) learning algorithm for an induction servomotor. The proposed AFC is comprised of a fuzzy controller and a robust controller. The fuzzy controller is to mimic an ideal controller and the robust controller is to dispel the effect of the approximation error between the fuzzy controller and the ideal controller. All the control parameters of the AFC are on-line tuned by a PI learning algorithm in the Lyapunov sense, thus the stability of the system can be guaranteed. Finally, a comparison between a fuzzy controller, an AFC with integral learning algorithm, and the proposed AFC with PI learning algorithm is presented. Simulation results verify that for the induction servomotor systems, the tracking performance of the AFC with PI learning algorithm is better than those of the fuzzy controller and the AFC with integral learning algorithm. Also, the convergence of the tracking error is speeded up.
On-line genetic fuzzy-neural sliding mode controller design
(2005)
Lin, PZ
;
Wang, WY
;
Lee, TT
;
Chen, GM
;
電控工程研究所
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.