This paper presents a novel event-triggered adaptive cognitive control to address the consensus problem of multi-agent systems (MASs) with a Leader under deception attacks. By using the reinforcement learning, adaptive radial basis function (RBF) neural networks and sliding mode control, an adaptive cognitive control is developed. This control has two parts: Actor and Critic. The Actor is designed by using adaptive RBF neural networks and sliding mode control, named adaptive sliding mode control. It is used to control the agent. The Critic is constructed utilizing the adaptive RBF neural networks, to evaluate the control performance of the Actor. In addition, to reduce the communication cost, an event triggered mechanism is designed. The Lyapunov stability analysis shows that the proposed event-triggered adaptive cognitive control can ensure the stabilization of the MASs in case of deception attacks. Simulations are performed to validate the feasibility of the proposed event-triggered adaptive cognitive control, indicating that it can decrease the effects of deception attacks and ensure that all Followers can synchronize to the Leader.

本文提出了一种新颖的事件触发自适应认知控制，以解决具有领导者在欺骗攻击下的多智能体系统 (MAS) 的共识问题。通过使用强化学习、自适应径向基函数 (RBF) 神经网络和滑模控制，开发了一种自适应认知控制。该控件有两部分：Actor 和 Critic。Actor是利用自适应RBF神经网络和滑模控制设计的，称为自适应滑模控制。它用于控制代理。Critic 是利用自适应 RBF 神经网络构建的，用于评估 Actor 的控制性能。此外，为了降低通信成本，设计了事件触发机制。Lyapunov 稳定性分析表明，所提出的事件触发自适应认知控制可以确保 MAS 在欺骗攻击情况下的稳定性。仿真验证了所提出的事件触发自适应认知控制的可行性，表明它可以减少欺骗攻击的影响并确保所有追随者都可以与领导者同步。