Cooperative Fault Diagnosis for Uncertain Nonlinear Multiagent Systems Based on Adaptive Distributed Fuzzy Estimators

This paper presents a cooperative fault diagnosis scheme for a class of uncertain nonlinear multiagent systems component and sensor faults in individual agents. Since the faulty system affects the healthy systems through interconnections, for each agent an estimator is designed to collect neighboring output estimations errors to consider its faulty effects on others, when computing its estimations for local state and faulty parameters. A new structure of distributed estimators is proposed by filtering regressor signals and sharing them among agents. Then, the sharings of signals are planned by properly constructing auxiliary graphs for undirected and directed networks. Two conditions are given to preselect estimators parameters for the convergences of the estimation errors. Unlike the existing results dealing with one common parameter with full state measurement and only for undirected graphs, this paper presents an output measurement-based approach for multiple parameters in undirected/directed networks. It shows that for the faults not providing persistent excitation in a signal agent, it is possible to estimate the faults exactly if the they excite all agents persistently. A simulation example of a group of single-link flexible-joint robots is given to verify the effectiveness of the proposed method.