中国空间科学技术2019，Vol.39Issue(4)：36-42,7.DOI:10.16708/j.cnki.1000‐758X.2019.0027
基于深度增强学习的卫星姿态控制方法
Sat elli t e atti tude control method based on deep reinf orcement l earni ng
摘要
Abstract
Aiming at the problem of sudden changes in the attitudes encountered by satellites while performing complex tasks such as discarding a payload or capturing a target ,a satellite attitude control method based on the deep reinforcement learning is proposed to restore the satellite to a stable state.Concretely , the attitude dynamics environment of the vehicle is firstly established , and the output of continuous control torque is discretized.Deep Q Network algorithm is then performed to train the autonomous attitude control of the satellite for further processing ,and the optimal intelligent output of discrete behavior is rewarded with the stabilization of attitude angular velocity.Finally ,the validity of the mechanism is verified by the simulation test.Results analysis illustrates that the deep reinforcement learning algorithm for satellite attitude control can stabilize satellite attitude after the satellite is disturbed by sudden random disturbance , and it can effectively solve the problem of traditional PD controller depending on the mass parameters of the controlled object.The proposed method adopts self‐learning to control the satellite attitude ,which has strong intelligence and universal applicability ,and has a strong application potential for future intelligent control of satellites performing complex space tasks.关键词
深度增强学习/卫星姿态控制/动力学环境/自主姿态控制/质量参数Key words
deep reinforcement learning / satellite attitude control / dynamic environment /autonomous attitude control /mass parameters分类
航空航天引用本文复制引用
王月娇,马钟,杨一岱,王竹平,唐磊..基于深度增强学习的卫星姿态控制方法[J].中国空间科学技术,2019,39(4):36-42,7.基金项目
国家自然科学基金(61702413) （61702413）
航天九院技术创新基金(2016JY06) （2016JY06）
