用神经网络动态调整轨道时间，让航天器安全对接
Constrained Control for Autonomous Spacecraft Rendezvous: Learning-Based Time Shift Governor
- 用LSTM学习历史状态，实时计算时间偏移量
- 在低地球轨道和莫尼利亚轨道均成功完成对接
- 计算效率提升，对接过程约束不被突破
本文提出一种基于时间偏移调节器（TSG）的控制方案，用于在二体问题框架下实现航天器交会与对接任务中的约束满足。作为主闭环系统的附加方案，TSG生成一个时移后的主航天器轨迹作为副航天器的目标参考。通过调整参考轨迹的时间偏移，确保约束始终满足，同时使时间偏移逐渐归零以完成对接。该方案采用长短期记忆（LSTM）神经网络，将时间偏移参数建模为过去主、副航天器状态序列的函数，并在离线仿真数据上进行训练。仿真结果表明，在低地球轨道（LEO）和莫尼利亚轨道上的交会任务中，该方法显著降低时间偏移计算耗时，且成功完成对接任务。
原文摘要 · Abstract (English)
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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