A data-driven subspace distributed fault detection strategy for linear heterogeneous multi-agent systems

Abstract

This paper presents a new data-driven subspace distributed fault detection strategy specifically designed for linear heterogeneous multi-agent systems (MASs). The proposed approach leverages the characteristics of heterogeneous MASs, where agents exhibit diverse dynamics and parameters. By utilizing subspace construction techniques, the proposed method captures the normal behavior of each agent and enables the detection of deviations that indicate the presence of faults. Unlike existing methods, the approach is completely data-driven and eliminating the need for centralized information or communication among the agents. Simulation results demonstrate the effectiveness and efficiency of the proposed approach in detecting simultaneous faults in different agents. Overall, the proposed approach represents a significant departure from existing methods and offers a powerful new tool for fault detection in heterogeneous multi-agent systems.