Satellite Attitude Control with Deep Reinforcement Learning

被引：1

作者：

Gao, Duozhi ^{[1
]}

Zhang, Haibo ^{[2
]}

Li, Chuanjiang ^{[1
]}

Gao, Xinzhou ^{[1
]}

机构：

[1] Harbin Inst Technol, Dept Control Sci & Engn, Harbin, Peoples R China

[2] Beijing Inst Control Engn, Beijing, Peoples R China

来源：

2020 CHINESE AUTOMATION CONGRESS (CAC 2020) | 2020年

关键词：

satellite attitude control; deep reinforcement learning; online optimization control; GAME; GO;

D O I：

10.1109/CAC51589.2020.9326605

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Deep reinforcement learning has recently gained great interest in the field of intelligent control. In this paper, we develop a set of deep reinforcement learning algorithms on satellite attitude control. By improving Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3), we analyze three kinds of attitude control problems, including no state-constrained, state-constrained and online optimization problems. We compare Deep Reinforcement Learning Controller with traditional PD controller at the same time. Besides, we summarize a design process to apply deep reinforcement learning algorithms on satellite attitude control problems. It is shown in this paper that Deep Reinforcement Learning Controller has advantages on model-free control and online optimization.

引用

页码：4095 / 4101

页数：7

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