Simplified ADP for Event-Triggered Control of Multiagent Systems Against FDI Attacks
针对系统状态不可测的多智能体系统，提出一种抗虚假数据注入攻击的最优控制方案，利用安全预选器提取未受攻击的输出数据，通过状态观测器识别系统状态，并采用事件触发机制减轻通信负担，在简化自适应动态规划框架下求解最优控制问题。
In this article, an optimal control scheme against false data injection (FDI) attacks for multiagent systems with unavailable system states is presented. The output data that remains uncorrupted is extracted from a group of output measurements during FDI attacks using a secure preselector. Then, on the basis of the unattacked output data, a state observer is utilized to identify system states. By taking the communication overhead into account, an event-triggered mechanism is employed to alleviate communication burdens. The solution of the optimal control problem related to event-triggered Hamilton–Jacobi–Bellman (HJB) equations is obtained within a simplified adaptive dynamic programming (ADP) framework. The key point is that a single network is introduced to successfully avoid the problem of repeated approximations in traditional dual networks. Note that the weight vectors in the single critic network are adjusted through experience replay (ER), which helps to avoid the restrictive persistence of excitation (PE) conditions. It is strictly proven that all the signals in the closed-loop system are uniformly ultimately bounded, and the critic network weight can converge to optimal values. Simulation results illustrate the effectiveness of the control scheme.