Hybrid method for accurate multi-gravity-assist trajectory design using pseudostate theory and deep neural networks

Bin, Yang and JingLang, Feng and XuXing, Huang and Shuang, Li (2022) Hybrid method for accurate multi-gravity-assist trajectory design using pseudostate theory and deep neural networks. Science China - Technological Sciences, 65 (3). pp. 595-610. ISSN 1674-7321 (https://doi.org/10.1007/s11431-021-1933-7)

[thumbnail of Bin-etal-SCTS-2021-Hybrid-method-for-accurate-multi-gravity-assist-trajectory-design-using-pseudostate-theory]
Preview
 Text. Filename: Bin_etal_SCTS_2021_Hybrid_method_for_accurate_multi_gravity_assist_trajectory_design_using_pseudostate_theory.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (1MB)| Preview

Abstract

This paper presents a novel hybrid method to design the continuous and accurate multi-gravity-assist trajectory for a high-fidelity dynamics. The gravitational perturbation of the primary body is considered during the gravity assistance. The pseudostate technique is applied to approximate the gravity-assisted trajectory, where the optimal sweepback duration is solved using a trained deep neural network. The major factors that affect the optimal sweepback duration of the approach and departure segments are investigated. The results show that the optimal sweepback duration of the approach segment only relies on the shape of the approach trajectory and is independent of the flight time. Then, a gravity-assisted trajectory patched strategy and a hybrid algorithm combining the particle swarm optimization and the sequential quadratic programming are developed to optimize the multi-gravity-assist trajectory. The proposed hybrid method is applied to the Europa orbiter mission. In comparison with the traditional patched conic method, this method demonstrates outstanding performance on accuracy and significantly reduces the computational time and complexity of the trajectory correction with the high-fidelity dynamics.

CORE (COnnecting REpositories)