Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

作     者：YANG Luyi LI Haiyang ZHANG Jin ZHU Yuehe YANG Luyi;LI Haiyang;ZHANG Jin;ZHU Yuehe

作者机构：College of Aerospace Science and EngineeringNational University of Defense TechnologyChangsha 410073China China Astronauts Research and Training CenterBeijing 100094China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace MissionsChangsha 410073China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2024年第35卷第2期

页      面：495-508页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 081104[工学-模式识别与智能系统] 082503[工学-航空宇航制造工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (12072365) the Natural Science Foundation of Hunan Province of China (2020JJ4657) 

主　　题：manned lunar mission free return orbit reachable domain(RD) deep neural network computation efficiency 

摘      要：It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.