基于深度神经网络的最优扰动长时间脉冲传输的快速逼近
Fast Approximation of Optimal Perturbed Long-Duration Impulsive Transfers via Deep Neural Networks
摘要：
多目标交会任务的设计需要一种方法来快速准确地近似任意两个交会目标之间的最优转移。本文提出了一种基于深度神经网络（DNN）的方法，用于快速逼近最优扰动长持续时间脉冲传输。根据分离体与交会目标升交点赤经差的变化趋势，将这种转移分为三种类型。一个有效的数据库生成方法结合可靠的优化方法。三个回归DNN被单独训练并应用于近似相应类型的传输。仿真结果表明，经过良好训练的DNN能够快速估计最佳速度增量，对于所有三种类型的传输，相对误差均小于小于3%。对总速度增量为几千m/s的碎片链的试验表明，该方法的估算结果与优化结果非常接近，最终估算误差小于10 m/s。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
