标题： 最优控制与神经网络在小行星会合任务分析中的比较研究 显示英文标题

标题： A Comparative Study of Optimal Control and Neural Networks in Asteroid Rendezvous Mission Analysis

摘要： 本文介绍了在初步小行星会合任务设计的背景下，最优控制方法和基于神经网络的估计器在应用性和准确性方面的比较研究。 所考虑的场景涉及一个配备低推力发动机的深空立方星，从地球出发，在三年的发射窗口内与近地小行星会合。 建立了包含变比冲、最大推力和路径约束的低推力轨迹优化模型。 通过顺序凸规划（SCP）结合解延续策略，高效地解决了最优控制问题。 神经网络框架包括两个模型：一个预测最小燃料消耗（$\Delta v$），另一个估计最小飞行时间（$\Delta t$），用于评估转移可行性。 案例结果表明，在没有路径约束的简化场景中，神经网络方法在大部分设计空间中实现了较低的相对误差，并成功捕捉了猪排图的主要结构特征。 在SCP-based延续方法由于存在多个局部最优而失效的情况下，神经网络仍能提供平滑且全局一致的预测，显著提高了早期小行星候选筛选的效率。 然而，由路径约束引起的可行区域变形导致某些边界区域出现明显差异，从而限制了网络在详细任务设计阶段的应用。 总体而言，将神经网络与猪排图分析相结合，为任务设计师和行星科学家提供了一个有效的决策工具，具有显著的工程应用潜力。 显示英文摘要

摘要： This paper presents a comparative study of the applicability and accuracy of optimal control methods and neural network-based estimators in the context of porkchop plots for preliminary asteroid rendezvous mission design. The scenario considered involves a deep-space CubeSat equipped with a low-thrust engine, departing from Earth and rendezvousing with a near-Earth asteroid within a three-year launch window. A low-thrust trajectory optimization model is formulated, incorporating variable specific impulse, maximum thrust, and path constraints. The optimal control problem is efficiently solved using Sequential Convex Programming (SCP) combined with a solution continuation strategy. The neural network framework consists of two models: one predicts the minimum fuel consumption ($\Delta v$), while the other estimates the minimum flight time ($\Delta t$) which is used to assess transfer feasibility. Case results demonstrate that, in simplified scenarios without path constraints, the neural network approach achieves low relative errors across most of the design space and successfully captures the main structural features of the porkchop plots. In cases where the SCP-based continuation method fails due to the presence of multiple local optima, the neural network still provides smooth and globally consistent predictions, significantly improving the efficiency of early-stage asteroid candidate screening. However, the deformation of the feasible region caused by path constraints leads to noticeable discrepancies in certain boundary regions, thereby limiting the applicability of the network in detailed mission design phases. Overall, the integration of neural networks with porkchop plot analysis offers a effective decision-making tool for mission designers and planetary scientists, with significant potential for engineering applications.