Journal of Systems Engineering and Electronics ›› 2026, Vol. 37 ›› Issue (5): 1737-1751.doi: 10.23919/JSEE.2026.000165

• CONTROL THEORY AND APPLICATION • Previous Articles    

The design of the orbit departing from Earth and rendezvousing near the Moon based on intelligent decision

Xiaotong Bu1,2(), Haiyang Li1,2,*(), Binyu Zhu1,2(), Luyi Yang3()   

  1. 1College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
    2State Key Laboratory of Space System Operation and Control, Changsha 410073, China
    3China Astronauts Research and Training Center, Beijing 100094, China
  • Received:2025-03-10 Online:2026-10-18 Published:2026-11-09
  • Contact: Haiyang Li E-mail:buxiaotong20@nudt.edu.cn;lihaiyang@nudt.edu.cn;1822350368@qq.com;yangluyi@nudt.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (12072365).

Abstract:

The flight plan of departing from the Earth and rendezvous near the Moon is a preferred strategy for future manned lunar exploration programs. However, significant decision-making challenges arise from diverse lunar insertion schemes during the Earth-Moon transfer process, where optimal energy consumption varies substantially across different orbital regimes. To address these challenges, an intelligent decision-making model employing deep convolutional neural networks (DCNNs) is proposed to select the optimal Earth-Moon transfer scheme, subsequently combine with the lunar rendezvous orbit to formulate an integrated mission profile. Results demonstrate that the intelligent decision-making approach achieves computational efficiency improvements of 3−4 orders of magnitude compared to conventional methods while maintaining comparable accuracy, confirming its practical viability for designing orbit of departing from the Earth and rendezvous near the Moon. This study offers valuable insights for future manned lunar missions and the application of intelligent methods in orbital design.

Key words: Earth-Moon transfer, lunar rendezvous, intelligent decision-making, deep convolutional neural network (DCNN)