Prior Knowledge-Augmented Broad Reinforcement Learning Framework for Fault Diagnosis of Heterogeneous Multiagent Systems

Abstract

A heterogeneous multiagent system (MAS) can easily experience unpredictable faults due to its complex structure and involvement of different individuals. However, existing approaches have several issues, including complicated network architecture, insufficient feature extraction, and poor generalization ability. This study proposes a novel framework called prior-knowledge-augmented broad reinforcement learning (PK-BRL) to effectively diagnose faults in a heterogeneous MAS. First, we construct a highly realistic visualized heterogeneous MAS and perfom fault injection. Second, we present a novel fault diagnosis framework based on broad reinforcement learning with prior knowledge that effectively integrates offline reinforcement learning and broad learning into the fault diagnosis process. The interaction between heterogeneous multiagents and the constructed environment enables us to learn a superior fault diagnosis strategy. Finally, experiments conducted on software-in-the-loop and hardware-in-the-loop platforms demonstrate that the proposed PK-BRL framework has a state-of-the-art diagnostic accuracy for the heterogeneous MAS, which offers valuable theoretical and practical significance for real-world application.