TY - JOUR
T1 - A Rapid Estimation for Interplanetary Low-Thrust Trajectories Using Support Vector Regression
AU - Xu, L.
AU - Shang, H.
AU - Qin, X.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/11/29
Y1 - 2018/11/29
N2 - During the preliminary phase of space mission planning and design, a large quantity of trajectory optimization problems have to be solved. Obtaining the optimal solutions of low-thrust trajectory is computationally challenging since the optimization problems usually involve an iterative numerical algorithm and the complicated numerical integration of the equations of motion. It is necessary to develop the rapid trajectory estimation methods for low-thrust transfer. In this paper, a new method based on machine learning has been proposed to estimate the optimal interplanetary low-thrust trajectory. The minimum-propellant low-thrust trajectory is optimized by using the hybrid optimization algorithm, which would provide the high-quality training samples for machine learning. Support vector regression is adopted to construct and train the estimation model. Numerical simulations demonstrate that the proposed estimation method and the percentage errors of random test samples are all lower than 5%. This application of machine learning method can accomplish very efficient low-thrust interplanetary trajectory evaluation and it is therefore suitable to extend the design flexibility in the practical exploration mission.
AB - During the preliminary phase of space mission planning and design, a large quantity of trajectory optimization problems have to be solved. Obtaining the optimal solutions of low-thrust trajectory is computationally challenging since the optimization problems usually involve an iterative numerical algorithm and the complicated numerical integration of the equations of motion. It is necessary to develop the rapid trajectory estimation methods for low-thrust transfer. In this paper, a new method based on machine learning has been proposed to estimate the optimal interplanetary low-thrust trajectory. The minimum-propellant low-thrust trajectory is optimized by using the hybrid optimization algorithm, which would provide the high-quality training samples for machine learning. Support vector regression is adopted to construct and train the estimation model. Numerical simulations demonstrate that the proposed estimation method and the percentage errors of random test samples are all lower than 5%. This application of machine learning method can accomplish very efficient low-thrust interplanetary trajectory evaluation and it is therefore suitable to extend the design flexibility in the practical exploration mission.
UR - http://www.scopus.com/inward/record.url?scp=85057738480&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/449/1/012020
DO - 10.1088/1757-899X/449/1/012020
M3 - Conference article
AN - SCOPUS:85057738480
SN - 1757-8981
VL - 449
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012020
T2 - 2018 2nd International Conference on Aerospace Technology, Communications and Energy Systems, ATCES 2018
Y2 - 15 September 2018 through 17 September 2018
ER -