Adaptive Event‐Triggered Resilient Control for Nonlinear MASs Under Unknown FDI Attacks via Reinforcement Learning

ABSTRACT In this paper, an adaptive resilient control scheme based on reinforcement learning (RL) is proposed for the control of nonlinear multi‐agent systems (MASs) under false data injection (FDI) attacks. When the system sensors are subject to unknown FDI attacks, traditional controller design methods are challenged by the inability to directly access all the original state information. To address this challenge, a novel coordinate‐error construction and Nussbaum‐type functions are incorporated into the control design, effectively attenuating the adverse impact of FDI attacks. Meanwhile, an adaptive event‐triggered mechanism (AETM) is developed to substantially reduce network communication burden. The controller is synthesized by integrating a critic function with an actor‐critic neural network‐based RL algorithm, enabling accurate online estimation of lumped uncertainties. Using Lyapunov stability theory, it is rigorously proven that the closed‐loop system is stable and that all signals remain bounded. Simulation results further corroborate the effectiveness and robustness of the proposed scheme, demonstrating improved control performance under FDI attacks with reduced communication load.