Adaptive fuzzy dynamic event-triggered consensus of multi-agent systems under sensor attacks

As information exchange among agents increases, multi-agent systems with limited communication and energy resources have become increasingly vulnerable to cyber threats, particularly sensor attacks that compromise data integrity and system stability. To address this challenge, this paper proposes a control framework for nonlinear multi-agent systems under sensor attacks, integrating adaptive fuzzy control with dynamic attack detection mechanism and dynamic event-triggered strategies. The proposed detection scheme uses only the local output errors of the agents without requiring knowledge of inter-agent static output mappings, thereby reducing implementation complexity. To achieve consensus tracking under attacks, an adaptive fuzzy consensus controller incorporating Nussbaum-type functions is developed within the backstepping framework to handle uncertain and time-varying output gains caused by attacks. Additionally, a dynamic event-triggered mechanism employing an auxiliary variable is proposed to significantly reduce communication overhead while preserving resilient consensus performance under sensor attacks. Rigorous theoretical analysis proves that all closed-loop signals remain bounded and consensus tracking is achieved despite the presence of attacks. Finally, simulation studies further demonstrate the proposed framework's effectiveness.