Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems

被引：73
|
作者：
Hsu, Chun-Fei [1 ]
Lin, Chih-Min [2 ]
Yeh, Rong-Guan [2 ]
机构：
[1] Tamkang Univ, Dept Elect Engn, New Taipei City 25137, Taiwan
[2] Yuan Ze Univ, Dept Elect Engn, Chungli 320, Taoyuan County, Taiwan
来源：
APPLIED SOFT COMPUTING | 2013年 / 13卷 / 04期
关键词：
Adaptive control; Sliding-mode control; Neural-fuzzy system; Chaotic system; Inverted pendulum; PARTICLE SWARM OPTIMIZATION; NETWORK CONTROL; TRACKING;
D O I：
10.1016/j.asoc.2012.12.028
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Many published papers show that a TSK-type fuzzy system provides more powerful representation than a Mamdani-type fuzzy system. Radial basis function (RBF) network has a similar feature to the fuzzy system. As this result, this article proposes a dynamic TSK-type RBF-based neural-fuzzy (DTRN) system, in which the learning algorithm not only online generates and prunes the fuzzy rules but also online adjusts the parameters. Then, a supervisory adaptive dynamic RBF-based neural-fuzzy control (SADRNC) system which is composed of a DTRN controller and a supervisory compensator is proposed. The DTRN controller is designed to online estimate an ideal controller based on the gradient descent method, and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the DTRN controller upon the system stability in the Lyapunov sense. Finally, the proposed SADRNC system is applied to control a chaotic system and an inverted pendulum to illustrate its effectiveness. The stability of the proposed SADRNC scheme is proved analytically and its effectiveness has been shown through some simulations. (C) 2013 Elsevier B. V. All rights reserved.
引用
收藏
页码：1620 / 1626
页数：7
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