Robust Fault Detection & Isolation in Distributed Dynamic Systems
Sam Nazari, Bahram Shafai · 2019
This paper considers robust fault detection in distributed consensus systems with agents that are subjected to simultaneous faults and unknown disturbances. First, a distributed system model is introduced so that the relationship between the agents and the unknown disturbances and the faults can be precisely captured. In this model, the local interactions are captured through an undirected graph and the collective dynamics are represented by a positive dynamic system. Next, necessary and sufficient conditions to decouple the unknown disturbances from the agents residual generators are derived in terms of an observer gain matrices. Specifically, it is shown that the problem of discriminating between unknown disturbances and faults in a distributed system under consensus dynamics can be reduced to the problem of determining a set of constraints on the spectrum of the residual generator coefficient matrices. The approach is illustrated through an example.