Intelligent tracking control of a DC motor driver using self-organizing TSK-type fuzzy neural networks |
| |
Authors: | Chun-Fei Hsu |
| |
Institution: | (1) College of Engineering, Chung Hua University, Hsinchu, 30012, Taiwan, ROC;(2) Department of Electrical Engineering, Tamkang University, No. 151, Yingzhuan Rd., Danshui Dist., New Taipei, 25137, Taiwan, ROC;(3) Department of Electrical Engineering, Chung Hua University, Hsinchu, 30012, Taiwan, ROC |
| |
Abstract: | In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing
approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary
knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then,
an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC) system which is composed of a neural controller and
a robust compensator is proposed. The neural controller uses an STFNN to approximate an ideal controller, and the robust compensator
is designed to eliminate the approximation error in the Lyapunov stability sense without occurring chattering phenomena. Moreover,
a proportional-integral (PI) type parameter tuning mechanism is derived to speed up the convergence rates of the tracking
error. Finally, the proposed ASTFNC system is applied to a DC motor driver on a field-programmable gate array chip for low-cost
and high-performance industrial applications. The experimental results verify the system stabilization and favorable tracking
performance, and no chattering phenomena can be achieved by the proposed ASTFNC scheme. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|