Fault Detection Approaches for Fuzzy Large-Scale Systems With Unknown Membership Functions

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 50; no. 9; pp. 3333 - 3343
• Main Authors: Wang, Huimin, Yang, Guang-Hong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cybernetics; Decentralized fault detection (FC); Fault detection; Fuzzy systems; Large-scale systems; Linear filters; Nonlinear systems; Parameter uncertainty; Scale models; Subsystems; Switches; Switching; switching-type filter; Uncertain systems; uncertain T–S fuzzy large-scale system; unknown membership functions; Upper bounds
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2018.2848672

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 <inline-formula> <tex-math notation="LaTeX">{H_{\infty }} </tex-math></inline-formula> 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.