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)（越南国家大学胡志明市分校胡志明市理工大学机械工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对受FDI攻击和输入饱和的非线性多智能体系统，提出基于积分强化学习的固定时间编队控制方法，实现有界控制与稳定收敛。
AI 中文摘要
研究了具有未知动力学、外部扰动以及执行器通道上虚假数据注入（FDI）攻击的非线性多智能体系统的领导者-跟随者编队控制问题。该问题被建模为零和微分博弈，并采用积分贝尔曼-艾萨克斯方法求解。为处理输入饱和约束，在优化问题中引入非二次控制代价函数，从而得到有界控制律。此外，本文提出一种代价函数构造方法，并设计评论家学习律，共同保证系统的实际固定时间稳定性，同时克服了现有固定时间强化学习公式的局限性。最后，严格证明了评论家权重估计误差和领导者参考编队跟踪误差均实际固定时间收敛到有界残差集。仿真结果验证了所提方法在外部扰动、FDI攻击和输入约束下的有效性。
英文摘要
The leader-follower formation control problem is investigated for nonlinear multi-agent systems with unknown dynamics, external disturbances, and false data injection (FDI) attacks on actuator channels. The problem is formulated as a zero-sum differential game and solved using the Integral Bellman-Isaacs approach. To address input saturation constraints, a non-quadratic control cost function is incorporated into the optimization problem, leading to a bounded control law. Furthermore, this paper proposes a cost function construction method and develops a critic learning law, which together guarantee the practical fixed-time stability of the system while overcoming the limitations of existing fixed-time reinforcement learning formulations. Finally, the practical fixed-time convergence of both the critic weight estimation error and the leader-referenced formation tracking error to bounded residual sets is rigorously proven. Simulation results demonstrate the effectiveness of the proposed method under external disturbances, FDI attacks, and input constraints.
Comments16 pages, 8 figures
