Deep reinforcement learning-based attitude control for spacecraft using control moment gyros

Abstract

This paper addresses the development of an attitude control system that steers control moment gyros (CMGs) based on deep reinforcement learning (DRL) for agile spacecraft. The proposed DRL-based attitude control system learns CMG steering strategies to achieve the desired attitude, thus potentially bypassing the singularity issues inherent in the CMG cluster. In particular, it is designed in two-phases to apply the DRL technique efficiently. In the first phase, the attitude control is performed based on DRL up to a certain tolerance, after which it switches to conventional control and steering law for stabilization in the second phase. The rapid pointing capability of the proposed DRL-based attitude control system is demonstrated for an agile spacecraft equipped with pyramid-type single gimbal control moment gyros. Additionally, in realistic scenarios of pointing multiple targets on the ground, the momentum vector recovery that the CMG system needs to consider is also briefly discussed.