Application of modern deep learning to satellite attitude control

卫星自主和机器学习是人们越来越感兴趣的研究领域。卫星姿态控制器通常是针对具有特定质量分布的卫星，利用特定的执行器，试图实现特定的姿态对准目标而开发的。虽然有通用控制器可用于各种卫星，如PID控制器，这些往往不能达到最佳性能。在卫星姿态控制中考虑深度强化学习是一个新的发展方向。

Satellite autonomy and machine learning are areas of growing research interest. Satellite attitude controllers are often developed for certain satellites which have specific mass distributions, which make use of a specific actuator, and attempt to achieve a specific attitude alignment objective. While there are general controllers which can be used for various satellites, such as PID controllers, these often do not achieve optimal performance. A new direction is to consider deep reinforcement learning for satellite attitude control.