Fast optimization method for Mars high-fidelity aerobraking trajectory using a neural network

被引：0
|
作者：
Yang B. [1 ,2 ]
Li S. [1 ,2 ]
Liu X. [1 ,2 ]
Huang X. [1 ,2 ]
Huang X. [1 ,2 ]
机构：
[1] College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Advanced Space Technology Laboratory, Nanjing University of Aeronautics and Astronautics, Nanjing
[3] Beijing Institute of Control Engineering, Beijing
来源：
| 1600年 / Chinese Academy of Sciences卷 / 50期
关键词：
Aerobraking; Mars exploration; Neural networks; Nonspherical gravitational perturbation; Trajectory optimization;
D O I：
10.1360/SST-2020-0245
中图分类号：
学科分类号：
摘要：
Aerobraking technology is a significant way to save fuel for interplanetary missions, and has been employed several times for Mars missions. A novel intelligent optimization method is proposed in this study to achieve high-fidelity aerobraking trajectory for Mars missions. First, a full-dimensional, nonspherical gravitational perturbation dynamic model is derived; this model is employed to analyze the sensitivity of the Mars aerobraking trajectory. The results indicate that the periapsis altitude of the aerobraking trajectory is volatile rather than being a constant value under the high-fidelity dynamic model. Then, two deep neural networks are applied to replace the complex dynamic equations for efficiently approximating the parameters of the aerobraking trajectory. Finally, a controlled aerobraking trajectory optimization approach is introduced for Mars missions, and the genetic algorithm is used to optimize the control variables. The numerical simulation results verify the effectiveness of the proposed method and show its outstanding advantages with respect to efficiency and accuracy. © 2020, Science Press. All right reserved.
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页码：1185 / 1199
页数：14
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