Minimal-order observer-based distributed fault detection and isolation for stochastic multi-agent systems

Abstract

In this paper, the problem of distributed fault detection and isolation (FDI) is investigated for a class of linear discrete-time stochastic multi-agent systems (MASs) with additive Gaussian white noises. By using the information received from the generalized neighbor agents, a set of residual generators are designed for one agent based on the minimal-order observers. After dividing the MAS into several first-order components, the residuals are designed to be robust to the faults in some designated components and sensitive to the faults in all the other components. Combining with FDI strategies, multiple concurrent faults in the generalized neighbor agents can be detected and isolated simultaneously. In addition, a necessary condition is established for the observer to have the minimum order. By means of the statistical method, a set of hypothesis tests are derived to detect and isolate the faults. Finally, a simulation example is presented to show the feasibility and effectiveness of the proposed methods.