基于时间序列聚类与强化学习的航天器惯性参数估计
Spacecraft inertial parameters estimation using time series clustering and reinforcement learning
AI总结：
本文提出结合时间序列聚类与强化学习优化驱动序列的机器学习方法，用于估计运行中惯性参数变化的航天器，并在多卫星部署场景中验证其对常见扰动的鲁棒性。
摘要：
本文提出一种机器学习方法，用于在航天器惯性参数在运行过程中发生变化的情况下对其进行估计，例如多次有效载荷部署、附件与吊杆展开、推进剂消耗，以及在轨服务和主动碎片清除操作期间。该机器学习方法结合时间序列聚类与由强化学习生成的优化驱动序列，以便于区分不同的惯性参数集。所提策略的性能在多卫星部署系统场景下进行了评估，结果表明该算法对此类操作中的常见扰动具有鲁棒性。
英文摘要：
This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations, e.g. multiple deployments of payloads, unfolding of appendages and booms, propellant consumption as well as during in-orbit servicing and active debris removal operations. The machine learning approach uses time series clustering together with an optimised actuation sequence generated by reinforcement learning to facilitate distinguishing among different inertial parameter sets. The performance of the proposed strategy is assessed against the case of a multi-satellite deployment system showing that the algorithm is resilient towards common disturbances in such kinds of operations.
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。
