Evaluating Gravity-Assist Range Set Based on Supervised Machine Learning

K. Zhang, H. Shang, Q. Chen, X. Qin

科研成果: 期刊稿件会议文章同行评审

1 引用 (Scopus)

摘要

The dynamics of gravity-assist (GA) trajectories contain strong nonlinearity, which makes the traditional methods for impulse transfer range set (RS) are intractable to deal with the gravity-assist RS. This paper develops a novel method to evaluate the gravity-assist RS based on regression methods in supervised machine learning (SML) field. The performances of three powerful regression methods with several common kernel functions are assessed. The Gaussian Processes Regression (GPR) method with Matérn 3/2 kernel is selected because of the minimum mean squared error (1.11×10-3 km2/s2). The predicting model based on GPR is constructed to make prediction form the orbital elements of destination orbits to the total velocity increment of corresponding optimal GA trajectories. The percentage error of predicting model is no more than 2%. Millions pairs of sample points are generated by the trained predicting model. The points with specified value of total velocity increment are extracted, of which the envelope constitutes the gravity-assist RS. Both of Venus GA and Mars GA trajectories are considered in this paper.

源语言英语
文章编号012021
期刊IOP Conference Series: Materials Science and Engineering
449
1
DOI
出版状态已出版 - 29 11月 2018
活动2018 2nd International Conference on Aerospace Technology, Communications and Energy Systems, ATCES 2018 - Shanghai, 中国
期限: 15 9月 201817 9月 2018

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Zhang, K., Shang, H., Chen, Q., & Qin, X. (2018). Evaluating Gravity-Assist Range Set Based on Supervised Machine Learning. IOP Conference Series: Materials Science and Engineering, 449(1), 文章 012021. https://doi.org/10.1088/1757-899X/449/1/012021