Paper Info
Title
A Fast Solution to Two-Impulse Lunar Transfer Trajectory based on Machine Learning Method.
Abstract
In this paper, the machine learning method is applied to predict the two-impulse lunar transfer velocity and orbital transfer window in a fast and efficient way. Firstly, the two-impulsive lunar transfer problem is described, and a trajectory design method is proposed to optimize the transfer based on Sequential Quadratic Programming algorithm. Then the geocentric relationship of the parking Low Earth Orbit (LEO) and the Moon is analyzed and the Earth-Centered Moon-to-Earth Plane Coordinate is introduced to transfer the inertial orbital elements into the pseudo ones. In this coordinate frame, domain knowledge suggests that the pseudo orbital elements can also serve as good learning features for the machine learning model. After generating the dataset, two machine learning models are established to estimate the orbital transfer window and orbital transfer velocity impulse respectively. Numerical simulations demonstrate that the machine learning models are efficient to estimate hundreds of transfer trajectories within 0.1 second, which shows much better performance than traditional orbit optimization method. The performance of two different machine learning algorithms is also assessed, where the neural network outperforms the gradient boosting model with the velocity impulse error less than 1m/s. It is also verified that the feature selection of pseudo orbital elements is appropriate which has smaller learning errors than that using the inertial orbital elements.
Year
DOI
Venue
2020
10.1109/CEC48606.2020.9185586
CEC
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Luyi Yang100.34
Ya-Zhong Luo2144.35
Haiyang Li3131.42
Jin Zhang401.01
Zhen Yang54513.51
Yue-he Zhu600.34