Event-Based Human-in-the-Loop Optimal Bipartite Consensus Control for Multiagent Systems Under False Data Injection Attacks

Abstract

This work primarily addresses the dynamic event-triggered human-in-the-loop (HiTL) optimal bipartite consensus control problem for nonlinear multiagent systems (MASs) under false data injection attacks. First, a supervisor is introduced to monitor the MASs, sending commands to leader to avoid emergencies. A zero-sum game model is then developed, where the attacker and the defender are treated as adversaries. Within the unified structure of zero-sum game theory, a cost function is defined to facilitate the determination of a Nash equilibrium. Furthermore, to reduce computational resources, a dynamic event-triggered Hamilton-Jacobi-Isaacs (HJI) equation is derived, along with a dynamic event-triggered mechanism. The solution to the HJI equation is learned using the critic neural network. The boundedness of all signals in the closed-loop systems is demonstrated. Additionally, the minimal inter-event time is proven to have a lower bound, thereby excluding Zeno behavior. Finally, the simulation results confirm the validity of the proposed approach.