Resilient Actuator Fault Estimation for Discrete-Time Complex Networks: A Distributed Approach

This article is concerned with the resilient fault diagnosis (FD) problem for a class of complex networks subject to possible loss of the actuator effectiveness and random variation of the filter gain. An unknown diagonal matrix is employed to characterize the multiplicative loss of actuator effectiveness for each node. The proposed filter utilizes the information from only the local node and the neighboring nodes. Since there is no need to have a center node receiving global information from every node, the developed FD algorithm is truly distributed. In the presence of gain variations, a time-varying filter is constructed to jointly estimate the system state and the loss of actuator effectiveness at each node. An upper bound of the filtering error covariance is calculated and then minimized via appropriately determining the filter gains. The filter is designed by solving two sets of recursive matrix equations, thereby meriting the suitability of online applications. Sufficient conditions are established to guarantee the exponential boundedness in mean square of the filtering error, and the monotonicity of the estimation error covariance with respect to the coupling strength is also investigated. An illustrative example is provided to show the usefulness of our FD strategy.

Automated summary

This article addresses the resilient fault diagnosis problem in complex networks with possible actuator effectiveness loss and filter gain variation, by utilizing a distributed filtering approach. The filter uses information from local and neighboring nodes, and is designed through solving two sets of recursive matrix equations for online applications.