The design of multitarget rendezvous missions requires a method to quickly and accurately approximate the optimal transfer between any two rendezvous targets. In this article, an artificial neural-network-based method is proposed for the rapid approximation of optimal perturbed long-duration impulsive transfers. The relationship between the optimal transfer velocity increments and the initial right ascension of the ascending node difference between the departure body and the rendezvous target is analyzed, and the result suggests that the perturbed long-duration impulsive transfers should be divided into three types. An efficient database generation method is developed. Three regression multilayer perceptrons (MLPs) are trained individually and applied to approximate the corresponding types of transfers. The simulation results show that the well-trained MLPs are capable of quickly estimating the optimal velocity increments with a relative error of less than 3% for all three types of transfers. Additional tests of the debris chains with total velocity increments of several thousand m/s show that the estimation results are very close to the optimized results, with a final estimation error of less than 10 m/s.

中文翻译：

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通过人工神经网络快速近似最佳扰动长时间脉冲传递

多目标会合任务的设计需要一种方法来快速，准确地估算任意两个会合目标之间的最佳传递。在本文中，提出了一种基于人工神经网络的方法，用于快速逼近最优扰动的长时冲动传递。分析了最优传递速度增量与出发物体与集合点之间的上升节点差的初始右提升之间的关系，结果表明，应将长期冲动传递分为三种类型。开发了一种有效的数据库生成方法。分别训练三个回归多层感知器（MLP），并将其应用于近似相应的传输类型。仿真结果表明，训练有素的MLP能够快速估计最佳速度增量，所有三种类型的传输的相对误差均小于3％。以总速度增量几千m / s进行的碎片链附加测试显示，估算结果与优化结果非常接近，最终估算误差小于10 m / s。