多无人机系统在线强化学习最优安全跟踪控制OA

In Unmanned Aerial Vehicle(UAV)formation tracking missions,False Data Injection(FDI)attackers can inject misleading data into the control commands,resulting in the fact that UAVs can not form the specified formation configuration,so there is a need to design a secure formation tracking controller.The attack-defense process was modeled as a zero-sum graphical game,in which the FDI attacker and the secure controller were viewed as game players.The attacker aims to maximize the cost function yet the secure controller serves a contrary purpose.Solving the game and acquiring the optimal secure control policy rely on solving the Hamilton-Jacobi-Isaacs(HJI)equation.The HJI equation is a coupled partial differential equation,which is difficult to solve directly.Therefore,the finite-time convergent online reinforcement learning algorithm that combines the experience replay mechanism was introduced and the critic-only neural network was utilized to approximate the value function for obtaining the optimal secure control policy.A numerical simulation was given to show the effectiveness of the raised scheme.