ROBUST FAULT DIAGNOSIS FOR A CLASS OF NONLINEAR SYSTEMS USING FUZZY-NEURAL AND SLIDING MODE APPROACHES

被引：0
|
作者：
Wu, Qing [1 ]
Saif, Mehrdad [1 ]
机构：
[1] Simon Fraser Univ, Vancouver, BC, Canada
来源：
2008 WORLD AUTOMATION CONGRESS PROCEEDINGS, VOLS 1-3 | 2008年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A robust fault diagnosis (FD) scheme integrating Takagi-Sugeno (T-S) fuzzy-neural models and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A fuzzy-neural observer and a fuzzy-neural sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of these two observers. Finally, the proposed FD scheme is applied to a satellite orbital control system. Simulation results show that this robust fault. diagnosis strategy is effective for a class of nonlinear systems.
引用
收藏
页码：455 / 460
页数：6
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