Fast Approximation of Optimal Perturbed Long-Duration Impulsive Transfers via Deep Neural Networks

TL;DR

This paper introduces a deep neural network approach to rapidly approximate optimal perturbed long-duration impulsive transfers for multitarget rendezvous missions, achieving high accuracy and efficiency.

Contribution

It develops a novel DNN-based method with an efficient database and optimization approach to estimate transfer parameters across different RAAN variation types.

Findings

DNNs estimate velocity increments with less than 3% relative error.

Estimated results are within 10 m/s of optimized solutions.

Method significantly speeds up transfer approximation for complex missions.

Abstract

The design of multitarget rendezvous missions requires a method to quickly and accurately approximate the optimal transfer between any two rendezvous targets. In this paper, a deep neural network (DNN)-based method is proposed for quickly approximating optimal perturbed long-duration impulsive transfers. This kind of transfer is divided into three types according to the variation trend of the right ascension of the ascending node (RAAN) difference between the departure body and the rendezvous target. An efficient database generation method combined with a reliable optimization approach is developed. Three regression DNNs are trained individually and applied to approximate the corresponding types of transfers. The simulation results show that the well-trained DNNs are capable of quickly estimating the optimal velocity increments with a relative error of less than 3% for all the three types of transfers. The tests on the debris chains with the total velocity increments of several thousand m/s show that the estimated results can be very close to the optimized ones with a final estimation error of less than 10 m/s.