控制理论与应用2025，Vol.42Issue(10)：1904-1913,10.DOI:10.7641/CTA.2024.30487
EP-DDPG引导的着舰控制系统
EP-DDPG guided carrier landing control system
摘要
Abstract
In order to improve the control accuracy of carrier-based aircraft in the longitudinal channel,this paper takes the deep deterministic policy gradient algorithm as the basic optimization framework to ensure that aircraft can land along the desired glide path with reasonable attitude and speed.An adaptive controller parameter adjustment strategy based on expert policy-deep deterministic policy gradient(EP-DDPG)algorithm is proposed.Firstly,building the MAGIC CARPET landing control system as the framework.Secondly,aiming at improving the adaptive ability and robustness of the controller,DDPG algorithm is designed based on the actor-critic framework to adjust the controller parameters online.Finally,in view of the low efficiency and poor effect of the early training of conventional reinforcement learning algorithm,an expert policy is constructed based on backward propagation(BP)neural network to provide guidance for the training of the agent,and a guidance exploration and coordination module is designed to make strategy decisions,so as to ensure the rationality of the action policy and the efficiency of the algorithm.The simulation results show that compared with the conventional controllers,the control precision and the robustness of the proposed algorithm are greatly improved.关键词
强化学习/深度确定性策略梯度算法/魔毯/行动者-评论家/BP神经网络Key words
reinforcement learning/deep deterministic policy gradient algorithm/MAGIC CARPET/actor-critic/BP neural network引用本文复制引用
雷元龙,谢鹏,刘业华,陈翃正,朱静思,盛守照..EP-DDPG引导的着舰控制系统[J].控制理论与应用,2025,42(10):1904-1913,10.基金项目
航空科学基金项目(20220058052002)资助.Supported by the Aeronautical Science Foundation Project(20220058052002). （20220058052002）
