Data-driven fault detection of heterogeneous multi-agent systems using combined hardware and temporal redundant information.

This study addresses the fault detection (FD) problem in heterogeneous multi-agent systems (HMASs) with unknown system models. A novel data-driven FD scheme is proposed by properly combining hardware and temporal redundant information to accelerate the generation of fault detectors while ensuring detection accuracy. The computational burden associated with the FD scheme is alleviated by applying a two-step order reduction algorithm. Additionally, an optimization problem is formulated, simplified and solved to achieve a compromise between sensitivity to faults and robustness to disturbances, further enhancing the detection performance of agents. Through a series of examples and comparative experiments, the effectiveness and improvements of the proposed approach are demonstrated.