基于强化学习的态度控制,用于杆电动帆
1State Key Laboratory of Mechanics and Control of Aerospace Structures, Nanjing University of Aeronautics and Astronautics, No.29 Yudao Street, Nanjing, Jiangsu 210016, China; College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, No.29 Yudao Street, Nanjing, Jiangsu 210016, China.
ISA transactions
|March 1, 2024
概括
一个新的强化学习 (RL) 控制方案有效地管理电能风帆 (E-sail) 态度. 这种先进的方法减少了计算时间,同时保持了无推进剂推进的控制精度.
科学领域:
- 航空航天工程 航空航天工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 电动太阳能风帆 (E-sails) 提供无推进剂的推进,但由于其非线性和不足的动力学,因此存在复杂的控制挑战.
- 传统的控制方法与E-sail系统复杂的态度控制要求作斗争.
研究的目的:
- 开发和评估一个基于强化学习 (RL) 的控制方案,用于控制杆E-帆系统的态度.
- 解决传统控制方案在管理电子帆系统方面的局限性.
主要方法:
- 设计了一个两阶段的RL控制策略,利用近接政策优化 (PPO) 算法和神经网络进行政策仿真和更新.
- 系统的态度动态被模拟使用非单元公式.
- 实时态度反控制是通过从学习的RL策略获得的状态到控制输出映射来实现的.
主要成果:
- 提出的基于RL的控制方案成功调节了绳索电压差异,以实现所需的E-sail态度.
- 模拟显示了RL控制器有效的态度调节能力.
- 与非线性模型预测控制 (NMPC) 的比较显示,在不影响控制精度的情况下,计算时间显著减少.
结论:
- 开发的基于RL的控制方案为E-sail attitude控制提供了高效和准确的解决方案.
- 这种方法为传统方法提供了一个有希望的替代方案,特别是在复杂的非线性系统中.
- 该研究强调了RL在推进无推进剂太空推进技术方面的潜力.
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