在地磁场中充电飞船形成的基于学习的重新配置
IEEE transactions on cybernetics
|October 17, 2024
概括
本研究提出了一种强化学习方法,用于使用洛伦兹增强技术飞行航天器. 它可以实现最佳控制和实时重新配置,适应动态空间环境.
科学领域:
- 航空航天工程 航空航天工程
- 控制理论 控制理论
- 天体动力学是指天体动力学.
背景情况:
- 飞行需要精确的相对运动控制.
- 洛伦兹力提供平衡状态,但不足以进行重新配置.
- 低推力机动对于动态阵列变化是必要的.
研究的目的:
- 开发一个最佳的控制框架,用于航天器形成的重新配置.
- 利用强化学习用于在地磁场中的自适应控制.
- 调查洛伦兹增强技术,以增强形成飞行.
主要方法:
- 推导非线性相对运动动力学,包括洛伦兹力,阻力和重力.
- 拉格朗的连贯结构分析用于识别平衡配置.
- 强化学习通过实时控制策略的最佳演示进行强化学习.
主要成果:
- 使用洛伦茨力来维持形成的证明平衡状态.
- 开发了一种强化学习剂,以实现最佳的阵营重新配置.
- 通过模拟验证了该方法的最佳性,稳定性和实时性能.
结论:
- 拟议的强化学习框架有效地实现了对航天器形成重新配置的最佳控制.
- 这种方法提高了在太空任务中适应不同环境条件的适应能力.
- 洛伦茨增强技术与低推力控制相结合,为未来的阵列飞行提供了一个有希望的解决方案.
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