自主航天器交会的约束控制：基于学习的时移调节器
Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor
AI总结：
针对二体问题下航天器交会对接的约束执行问题，提出集成LSTM网络的时移调节器控制方案，离线训练后可缩短时移参数计算时间，在近地轨道与闪电轨道任务中验证了有效性。
摘要：
本文提出一种基于时移调节器（Time Shift Governor, TSG）的控制方案，用于在二体问题场景下的交会对接（rendezvous and docking, RD）任务中强制执行约束。作为标称闭环系统的附加方案，TSG生成经过时移的主航天器（Chief spacecraft）轨迹，作为副航天器（Deputy spacecraft）的目标参考轨迹。这种对指令参考轨迹的修改可确保约束得到执行，同时时移量逐步减小至零以完成交会。我们的TSG实现方法集成了LSTM神经网络，该网络将时移参数近似为副航天器与主航天器过去状态序列的函数。该LSTM神经网络基于仿真数据进行离线训练。我们报告了近地轨道（Low Earth Orbit, LEO）与闪电轨道（Molniya orbit）上RD任务的仿真结果，以验证所提控制方案的有效性。该方案在大多数场景下缩短了时移参数的计算时间，并成功完成了交会任务。
英文摘要：
This paper develops a Time Shift Governor (TSG)-based control scheme to enforce constraints during rendezvous and docking (RD) missions in the setting of the Two-Body problem. As an add-on scheme to the nominal closed-loop system, the TSG generates a time-shifted Chief spacecraft trajectory as a target reference for the Deputy spacecraft. This modification of the commanded reference trajectory ensures that constraints are enforced while the time shift is reduced to zero to effect the rendezvous. Our approach to TSG implementation integrates an LSTM neural network which approximates the time shift parameter as a function of a sequence of past Deputy and Chief spacecraft states. This LSTM neural network is trained offline from simulation data. We report simulation results for RD missions in the Low Earth Orbit (LEO) and on the Molniya orbit to demonstrate the effectiveness of the proposed control scheme. The proposed scheme reduces the time to compute the time shift parameter in most of the scenarios and successfully completes rendezvous missions.
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