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

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2 Author(s)
Qing Wu ; Simon Fraser Univ., Vancouver, BC ; Saif, M.

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.

Published in:

Automation Congress, 2008. WAC 2008. World

Date of Conference:

Sept. 28 2008-Oct. 2 2008

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