A neural-fuzzy sliding mode observer for robust fault diagnosis
Document Type
Conference Proceeding
Publication Date
11-23-2009
Publication Title
Proceedings of the American Control Conference
First Page
4982
Last Page
4987
Abstract
A robust fault diagnosis (FD) scheme using Takagi-Sugeno (T-S) neural-fuzzy model and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A neural-fuzzy observer and neural-fuzzy sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of the two observers. Stability of the observers are analyzed as well. Finally, the proposed FD scheme using these observers is applied to a point mass satellite orbital control system example. Numerical simulation results show that this robust fault diagnosis strategy is effective for the considered class of nonlinear systems. © 2009 AACC.
DOI
10.1109/ACC.2009.5160193
ISSN
07431619
ISBN
9781424445240
Recommended Citation
Wu, Qing and Saif, Mehrdad. (2009). A neural-fuzzy sliding mode observer for robust fault diagnosis. Proceedings of the American Control Conference, 4982-4987.
https://scholar.uwindsor.ca/electricalengpub/358