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

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 ∞ 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.