Adaptive RCMAC sliding mode control for uncertain nonlinear systems

被引：0
|
作者：
Lin, CM [1 ]
Chen, CH [1 ]
机构：
[1] Yuan Ze Univ, Dept Elect Engn, Taoyuan 32003, Taiwan
来源：
NEURAL COMPUTING & APPLICATIONS | 2006年 / 15卷 / 3-4期
关键词：
adaptive control; recurrent cerebellar-model-articulation-controller (RCMAC); sliding-mode control (SMC); chaotic system; car-following system;
D O I：
10.1007/s00521-006-0027-0
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An adaptive recurrent cerebellar-model-articulation-controller (RCMAC) sliding-mode control (SMC) system is developed for the uncertain nonlinear systems. This adaptive RCMAC sliding-model control (ARCSMC) system is composed of two systems. One is an adaptive RCMAC system utilized as the main controller, in which an RCMAC is designed to identify the system models. Another is a robust controller utilized to achieve system's robust characteristics, in which an uncertainty bound estimator is developed to estimate the uncertainty bound so that the chattering phenomenon of control effort can be eliminated. The on-line adaptive laws of the ARCSMC system are derived in the sense of Lyapunov so that the system stability can be guaranteed. Finally, a comparison between SMC and ARCSMC for a chaotic system and a car-following system are presented to illustrate the effectiveness of the proposed ARCSMC system. Simulation results demonstrate that the proposed control scheme can achieve favorable control performances for the chaotic system and car-following systems without the knowledge of system dynamic functions.
引用
收藏
页码：253 / 267
页数：15
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