Decentralized Fault Detection for Affine T–S Fuzzy Large-Scale Systems With Quantized Measurements

Abstract

In this paper, the decentralized fault detection (FD) problem is discussed for affine Takagi-Sugeno(T-S) fuzzy large-scale systems with quantized measurements. For each subsystem of the fuzzy large-scale system, an FD filter is constructed with local measurements. The filter synthesis considers the unmatched regions between the subsystem and the filter because of the quantization errors. A cyclic-small-gain condition is introduced to address the unknown interconnections such that the resulting augmented system is asymptotically stable and satisfies an $H_{\infty }$ -norm bound constraint. With the introduced slack matrices, novel decentralized FD filter synthesis conditions are obtained in the form of linear matrix inequalities. Compared with the existing results, the proposed decentralized FD scheme avoids the use of communication among interconnected subsystems and can better detect the fault when quantization errors exist. Finally, a numerical example is used to illustrate the effectiveness and merits of the proposed method.