Horizon-Dependent Tube MPC for Elliptical-Orbit Rendezvous Under Mass Uncertainty
作者: Omer Burak Iskender, Keck-Voon Ling
分类: eess.SY, astro-ph.IM, physics.space-ph
发布日期: 2026-08-27
备注: 19 pages, 12 figures, 8 tables. Accepted for presentation at the 77th International Astronautical Congress (IAC 2026), Antalya, Turkiye, 5-9 October 2026; paper IAC-26,C1,4,9,x108305. Accepted version (authors' preprint), not the version of record. Typeset in a plain article class rather than the IAF conference template
💡 一句话要点
提出基于视距依赖的管道模型预测控制以解决轨道交会中的质量不确定性问题
🎯 匹配领域: 支柱一：机器人控制 (Robot Control)
关键词: 管道模型预测控制 轨道交会 质量不确定性 推进剂优化 航天器控制 深空探测 安全保证
📋 核心要点
- 现有控制方法在长距离飞行时可能失去安全保证，导致推进剂浪费和任务失败。
- 论文提出了一种基于视距依赖的管道模型预测控制方法，通过设计规则确保控制的安全性。
- 实验结果显示，该方法在多个轨道条件下显著节省推进剂，并保持高精度的捕获能力。
📝 摘要（中文）
本文探讨了一种航天器在接近目标时的控制策略，特别是在从300公里距离接触目标的过程中，如何确保控制的安全性。研究提出了一种设计规则，通过比较预测模型的线性化误差与控制器需抵消的干扰集，确定了保证失效的范围。该方法在火星样本返回任务中应用，优化了推进剂的使用，并通过重新定位相对轨道元素恢复了控制保证。实验表明，该控制器在圆形和偏心轨道上分别节省了29%和40%的推进剂，同时满足了捕获要求。
🔬 方法详解
问题定义：本文旨在解决航天器在接近目标时的控制安全性问题，现有方法在长距离飞行中可能失去保证，导致推进剂的浪费和任务风险增加。
核心思路：论文提出了一种新的设计规则，通过比较预测模型的线性化误差与控制器需抵消的干扰集，确定安全保证失效的范围，从而优化控制策略。
技术框架：整体架构包括三个主要阶段：首先，确定干扰集和线性化误差；其次，应用设计规则进行控制器设计；最后，通过重新定位相对轨道元素来恢复控制保证。
关键创新：最重要的创新在于将预测视距与可行性紧密结合，使得视距搜索限制成为任务参数，而非求解器设置，从而提高了控制的安全性和有效性。
关键设计：关键参数包括采样周期、轨道信息和干扰界限，设计中使用了递归可行性和渐近稳定性的方法来确保控制器的性能。
🖼️ 关键图片
📊 实验亮点
实验结果表明，控制器在圆形目标轨道上节省了29%的推进剂，在偏心轨道上节省了40%。在每次实验中，控制器均能在0.20米的捕获要求内成功对接，且中位数偏差约为5厘米，显示出优异的性能。
🎯 应用场景
该研究具有广泛的应用潜力，尤其是在深空探测和卫星对接等任务中。通过优化推进剂的使用和提高控制的安全性，能够显著提升航天器的任务成功率和经济效益，未来可能推动更多复杂航天任务的实现。
📄 摘要（原文）
A spacecraft closing on a target from three hundred kilometres to contact flies one guidance law across five orders of magnitude of range, and a controller that is provably safe at close range can lose that guarantee completely at long range while continuing to fly as though nothing were wrong. This paper derives the range at which the guarantee lapses and uses it as a design rule. The bound compares the prediction model's own linearisation error against the disturbance set the controller was built to reject, and needs only the sampling period, the orbit and that disturbance bound, so it can be evaluated before any simulation. On a Mars Sample Return approach it disqualifies the homing phase, where most of the propellant is spent, and clears the other two. Re-posing the disqualified phase in relative orbital elements restores the guarantee; re-posing a phase the rule already clears, in a frame two orders of magnitude more accurate, changes propellant by under a tenth of one per cent, and it is that second prediction that makes the rule falsifiable rather than descriptive. The constraint tightening also ties the prediction horizon to feasibility, so the horizon search limit becomes a mission parameter rather than a solver setting. Against a reimplementation of a published benchmark that reproduces its propellant to within one per cent, over five hundred dispersed Monte Carlo transfers per case on matched seeds, the controller saves 29% of the propellant on a circular target orbit and 40% on an eccentric one, docking inside the 0.20 m capture requirement on essentially every draw at a median miss near 5 cm. Two results run the other way: the saving is bought with time of flight and computation, and it comes from what the guarantee demanded of the terminal condition rather than from better disturbance rejection. Recursive feasibility and asymptotic stability are not claimed.