Secure Tracking Control via Fixed-Time Convergent Reinforcement Learning for a UAV CPS  

作　　者：Zhenyu Gong Feisheng Yang 

机构地区：[1]the School of Automation,Northwestern Polytechnical University,Xi’an 710129 [2]the Research&Development Institute of Northwestern Polytechnical University in Shenzhen,Shenzhen 518063,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第7期1699-1701,共3页自动化学报（英文版）

基　　金：supported partially by Guangdong Basic and Applied Basic Research Foundation (2023A1515 011220);National Natural Science Foundation of China (62073269);Key Research and Development Program of Shaanxi (2022GY-244);Aeronautical Science Foundation of China (2020Z034053002);Natural Science Foundation of Chongqing,China (CSTB2022NSCQMSX0963)。

摘　　要：Dear Editor,This letter is concerned with the secure tracking control problem in the unmanned aerial vehicle(UAV) system by fixed-time convergent reinforcement learning(RL). By virtue of the zero-sum game,the false data injection(FDI) attacker and secure controller are viewed as game players.

关 键 词：SECURE Fixed false 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TP273[自动化与计算机技术—检测技术与自动化装置] TP181[自动化与计算机技术—控制科学与工程]