Fault detection for non-Gaussian stochastic distribution fuzzy systems by an event-triggered mechanism

This paper studies an event-triggered fault detection (FD) problem for non-Gaussian stochastic distribution fuzzy systems. Different from other systems, the available information of the stochastic distribution systems is the measurable output probability density functions (PDFs) rather than the output itself. This increases the difficulty of the event-triggered-based observer synthesis. To overcome the difficulty, a new event-triggered observer approach based on the information of the output PDFs is proposed. First, a B-spline model is employed to approximate the output PDFs. Second, a novel event-triggered scheme (ETS) is designed to save the limited communication source. Then, a finite-frequency H_∕L_∞ fault detection observer is constructed such that the effect of the PDFs approximation error on the residual signal can be attenuated and the FD performance can be increased. Finally, two examples are presented to demonstrate the effectiveness of the proposed method.