Prescribed-Time Optimal Resilient Consensus Control for Nonlinear Uncertain Multiagent Systems
针对遭受拒绝服务攻击的非线性不确定多智能体系统，提出一种基于强化学习的预设时间最优一致性控制方法，通过弹性事件触发通信平衡收敛速度与控制成本。
This article addresses the problem of prescribed-time optimal consensus control for a class of uncertain nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks over resilient event-triggered communication (RETC). First, a novel performance index function is devised to balance the convergence of consensus error and the cost of control energy, thereby achieving the optimal consensus with the preassigned steady-state precision and convergence time. Subsequently, a set of intermediate variables is established within the framework of reinforcement learning (RL)-based optimized backstepping to solve the problem of unknown control gains, which facilitates the implementation of optimal controllers. Furthermore, the designed actual optimal control law is transmitted through RETC, which provides two advanced advantages: 1) a resilient switching control protocol to avoid the impact of DoS attacks and 2) a flexible switching threshold event-triggered mechanism to conserve communication resources and balance system performance. Finally, the effectiveness of the presented approach is verified by a simulation example.