TY - JOUR
T1 - Evaluating Gravity-Assist Range Set Based on Supervised Machine Learning
AU - Zhang, K.
AU - Shang, H.
AU - Chen, Q.
AU - Qin, X.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/11/29
Y1 - 2018/11/29
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85057763031&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/449/1/012021
DO - 10.1088/1757-899X/449/1/012021
M3 - Conference article
AN - SCOPUS:85057763031
SN - 1757-8981
VL - 449
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012021
T2 - 2018 2nd International Conference on Aerospace Technology, Communications and Energy Systems, ATCES 2018
Y2 - 15 September 2018 through 17 September 2018
ER -