Distributed Dynamic Event-Triggered Control for Multiagent Systems Under FDI Attack via ESN-Based Adaptive Dynamic Programming

In this article, a secure consensus control scheme based on adaptive dynamic programming is proposed for multiagent systems under false data injection (FDI) attacks. By the adaptive sliding mode observer, the attack estimator is designed to compensate for the FDI attack. Subsequently, the secure consensus control problem is recast as an optimal control problem. To save network resources, the dynamic event-triggered mechanism is introduced into the design of the optimal control law. Acquiring the optimal event-triggered control (ETC) policy is related to solving the coupled Hamilton-Jacobi–Bellman equation. Based on the dual heuristic programming technique and single critic neural network (NN) structure, the echo state network is employed in approximating the optimal ETC strategy. The experience replay mechanism is utilized to design the NN weight updating law, which can remove the persistence of excitation conditions. Then, the closed-loop stability and Zeno behavior avoidance are analyzed. The simulation is provided to support the effectiveness of the presented method.