受FDI攻击与扰动影响的饱和未知非线性多智能体系统的预定时间积分强化学习

中文摘要 AI 辅助

本文研究了在执行器约束、外部扰动和虚假数据注入（FDI）攻击下，未知非线性多智能体系统的安全领导者-跟随者编队问题。图耦合的协调误差动态被建模为局部零和微分博弈，其中非二次输入效用函数产生与饱和兼容的安全策略，而执行器通道的FDI和扰动作为对抗性输入。为消除对未知非线性漂移的显式依赖，积分贝尔曼-艾萨克斯恒等式使得仅基于评论家网络即可从有限轨迹数据中学习。双幂状态代价结构和带截止期限参数的评论家更新将最优学习与预定时间镇定相结合。与固定时间方法（其收敛时间上界由预选增益决定）不同，所提框架首先分配总截止期限，并将其在数据信息性、评论家学习、强化窗口和编队收敛之间进行分配。评论家误差和编队误差在独立于初始条件的情况下实际预定时间收敛到有界残差集，同时通过构造满足安全执行器约束。仿真在FDI攻击、扰动、输入约束和不同初始条件下验证了该框架。

英文摘要

This paper addresses secure leader-follower formation of unknown nonlinear multi-agent systems under actuator constraints, external disturbances, and false-data-injection (FDI) attacks. The graph-coupled coordination-error dynamics are formulated as local zero-sum differential games, where a nonquadratic input utility yields saturation-compatible secure policies and actuator-channel FDI and disturbances act as adversarial inputs. To eliminate explicit dependence on the unknown nonlinear drift, an integral Bellman-Isaacs identity enables critic-only learning from finite trajectory data. A two-power state-cost structure and a deadline-parameterized critic update connect optimal learning with predefined-time stabilization. Unlike fixed-time methods whose settling-time bound is determined by preselected gains, the proposed framework assigns the overall deadline first and allocates it among data informativity, critic learning, the reinforcement window, and formation convergence. Practical predefined-time convergence of the critic and formation errors to bounded residual sets is established independently of initial conditions, while secure actuator constraints are satisfied by construction. Simulations validate the framework under FDI attacks, disturbances, input constraints, and different initial conditions.

发表机构

• Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City (VNU-HCM)（胡志明市理工大学机械工程学院，越南国立大学胡志明市分校）

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