利用门控循环单元实现大地测量任务迭代式在线精确姿态控制
Leveraging Gated Recurrent Units for Iterative Online Precise Attitude Control for Geodetic Missions
AI总结：
本文提出利用门控循环单元（GRU）学习并预测外部扰动趋势，通过加性校正项迭代改进PID控制器，以提升GRACE-FO等大地测量任务的姿态控制精度，仿真验证了姿态误差显著减小。
摘要：
本文研究了大地测量任务（如GRACE Follow-on（GRACE-FO）任务）中的精确姿态控制问题。传统且成熟的控制方法，如比例-积分-微分（PID）控制器，一直是包括GRACE-FO任务在内的大多数航天任务姿态控制的标准。我们没有大幅修改（或替换）这些任务中使用的原始PID控制器，而是引入了一种对PID控制器的迭代式修改，以确保提高姿态控制精度（即减小姿态误差）。所提出的修改利用门控循环单元（GRU）来学习并预测从GRACE卫星传入的姿态测量数据中得出的外部扰动趋势。我们的分析揭示了外部扰动时间序列数据中的明显趋势，表明GRU在预测作用于系统的未来扰动方面具有潜在效用。学习到的GRU模型通过一个定期更新的加性校正项，在标准PID控制回路内实时补偿这些扰动。仿真结果验证了姿态误差的显著减小，证实了我们所提出方法的有效性。
英文摘要：
In this paper, we consider the problem of precise attitude control for geodetic missions, such as the GRACE Follow-on (GRACE-FO) mission. Traditional and well-established control methods, such as Proportional-Integral-Derivative (PID) controllers, have been the standard in attitude control for most space missions, including the GRACE-FO mission. Instead of significantly modifying (or replacing) the original PID controllers that are being used for these missions, we introduce an iterative modification to the PID controller that ensures improved attitude control precision (i.e., reduction in attitude error). The proposed modification leverages Gated Recurrent Units (GRU) to learn and predict external disturbance trends derived from incoming attitude measurements from the GRACE satellites. Our analysis has revealed a distinct trend in the external disturbance time-series data, suggesting the potential utility of GRU's to predict future disturbances acting on the system. The learned GRU model compensates for these disturbances within the standard PID control loop in real time via an additive correction term which is updated at regular time intervals. The simulation results verify the significant reduction in attitude error, verifying the efficacy of our proposed approach.
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。
