航空科学技术2024，Vol.35Issue(4)：25-30,6.DOI:10.19452/j.issn1007-5453.2024.04.004
多无人机系统在线强化学习最优安全跟踪控制
Optimal Secure Tracking Control in Multi-UAVs Based on Online Reinforcement Learning
摘要
Abstract
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.关键词
FDI攻击/多无人机/在线强化学习/优化控制/零和图博弈Key words
FDI attack/multi-UAVs/online reinforcement learning/optimal control/zero-sum graphical game分类
航空航天引用本文复制引用
弓镇宇,杨飞生..多无人机系统在线强化学习最优安全跟踪控制[J].航空科学技术,2024,35(4):25-30,6.基金项目
国家自然科学基金(62073269) （62073269）
航空科学基金(2020Z034053002) （2020Z034053002）
陕西省重点研发计划项目(2022GY-244) （2022GY-244）
重庆市自然科学基金(CSTB2022NSCQ-MSX0963) （CSTB2022NSCQ-MSX0963）
广东省基础与应用基础研究基金(2023A1515011220) National Natural Science Foundation of China (62073269) （2023A1515011220）
Aeronautical Science Foundation of China(2020Z034053002) （2020Z034053002）
Key Research and Development Program of Shaanxi (2022GY-244) （2022GY-244）
Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0963) （CSTB2022NSCQ-MSX0963）
Guangdong Basic and Applied Basic Research Foundation(2023A1515011220) （2023A1515011220）
