Scholarship at UWindsor

Title

Robust fault diagnosis for a class of nonlinear systems using fuzzy-neural and sliding mode approaches

Authors

Qing Wu, Simon Fraser University
Mehrdad Saif, Simon Fraser University

Document Type

Conference Proceeding

Publication Date

12-1-2008

Publication Title

2008 World Automation Congress, WAC 2008

Abstract

Arobust 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.

ISBN

9781889335384

Recommended Citation

Wu, Qing and Saif, Mehrdad. (2008). Robust fault diagnosis for a class of nonlinear systems using fuzzy-neural and sliding mode approaches. 2008 World Automation Congress, WAC 2008.
https://scholar.uwindsor.ca/electricalengpub/363