EP-DDPG guided carrier landing control system
LEI Yuan-long
XIE Peng
LIU Ye-hua
CHEN Hong-zheng
ZHU Jing-si
SHENG Shou-zhao
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.
Keywords：reinforcement learningdeep deterministic policy gradient algorithmMAGIC CARPETactor-criticBP neural network
Publication Date：2025-10-30
Online Publishing Date：2025-11-13(First online date of this platform, not the publication date of the document)
Pages：10( 1904-1913 )