TSK Fuzzy CMAC-Based Robust Adaptive Backstepping Control for Uncertain Nonlinear Systems

被引：60

作者：

Lin, Chih-Min ^{[1
]}

Li, Hsin-Yi ^{[1
]}

机构：

[1] Yuan Ze Univ, Dept Elect Engn, Tao Yuan 320, Taiwan

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2012年 / 20卷 / 06期

关键词：

Backstepping control; chaotic system; cerebellar model articulation controller (CMAC); Takagi-Suegeno-Kang (TSK) fuzzy system; voice coil motor (VCM); PARTICLE SWARM OPTIMIZATION; OUTPUT-FEEDBACK CONTROL; SLIDING-MODE CONTROL; NEURAL-NETWORK; DESIGN; HYBRID; MOTOR; SYNCHRONIZATION;

D O I：

10.1109/TFUZZ.2012.2191789

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A Takagi-Suegeno-Kang (TSK) fuzzy cerebellar-model-articulation-controller-based robust adaptive backstepping (TFCRAB) control system is proposed for the uncertain nonlinear systems. This TFCRAB control system is composed of a novel TSK fuzzy cerebellar model articulation controller (TFC) and a robust compensator. The proposed TFC is a generalization of a TSK fuzzy system, a fuzzy neural network, and a conventional cerebellar-model-articulation-controller. It is used as the principal tracking controller to mimic an ideal backstepping controller (IBC). The parameters of TFC are tuned online by the derived adaptation laws based on the Lyapunov stability theorem. The robust compensator is designed to dispel the approximation error between the TFC and the IBC so that the asymptotic stability of the closed-loop system can be guaranteed. Finally, the proposed control system is applied to control a Duffing-Holmes chaotic system and a voice coil motor. From the simulation and experimental results, it is verified that the proposed TFCRAB control scheme can achieve favorable tracking performance and that even the system models of the controlled systems are unknown.

引用

页码：1147 / 1154

页数：8

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