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Fault Detection Approaches for Fuzzy Large-Scale Systems With Unknown Membership Functions.
- Source :
-
IEEE Transactions on Systems, Man & Cybernetics. Systems . Sep2020, Vol. 50 Issue 9, p3333-3343. 11p. - Publication Year :
- 2020
-
Abstract
- This paper investigates the decentralized fault detection (FD) problem within a type of nonlinear large-scale systems under the parameter uncertainties constraints. First of all, a nonlinear system is treated as the T–S fuzzy large-scale model with unknown membership functions. Then, a switching method is employed in the FD filter integration. Combining the local measurements of each subsystem and the lower and upper bounds information collected from the unknown membership functions, a new decentralized FD filter is built. A cyclic-small-gain condition is introduced to guarantee that the resulted augmented FD system is asymptotically stable with a satisfying ${H_{\infty }}$ performance. The comparison results show that the proposed switching-type decentralized FD filter can achieve a better FD performance than linear filters. Finally, the validity and superiority of the proposed method are verified with two examples. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21682216
- Volume :
- 50
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Systems, Man & Cybernetics. Systems
- Publication Type :
- Academic Journal
- Accession number :
- 145287272
- Full Text :
- https://doi.org/10.1109/TSMC.2018.2848672