用强化学习实现带指向禁区的航天器姿态控制，提升安全性与自主性。
Deep reinforcement learning-based spacecraft attitude control with pointing keep-out constraint
- 采用SAC算法处理连续状态和动作空间，设计新状态表示显式编码姿态约束区。
- 通过奖励函数设计确保在指定禁区外完成姿态调整，仿真验证有效。
- 适合需要高自主性、复杂约束下的航天器控制系统研发人员参考。
本文针对带有单一指向禁区的航天器姿态重定向控制问题，采用深度强化学习（DRL）方法。使用Soft Actor-Critic（SAC）算法处理连续状态与动作空间，并设计一种新的状态表示，以显式包含姿态约束区域的紧凑表达。奖励函数被构建为在达成控制目标的同时强制执行姿态约束。通过课程学习策略进行智能体训练。仿真结果表明，所提出的基于DRL的方法在航天器指向受限的姿态控制中具有有效性。
原文摘要 · Abstract (English)
This paper implements deep reinforcement learning (DRL) for spacecraft reorientation control with a single pointing keep-out zone. The Soft Actor-Critic (SAC) algorithm is adopted to handle continuous state and action space. A new state representation is designed to explicitly include a compact representation of the attitude constraint zone. The reward function is formulated to achieve the control objective while enforcing the attitude constraint. A curriculum learning approach is used for the agent training. Simulation results demonstrate the effectiveness of the proposed DRL-based method for spacecraft pointing-constrained attitude control.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。