Qu Qingyu, Geng Lian, Liu Kexin, Lu Jinhu
IEEE Trans Cybern. 2025 Feb;55(2):588-599. doi: 10.1109/TCYB.2024.3476078. Epub 2025 Feb 3.
This article introduces a novel approach for spacecraft formation flying utilizing Lorentz-augmented techniques. It demonstrates that the relative motion among spacecraft, driven by the Lorentz force, possesses equilibrium states beneficial for formation maintenance. However, for effective formation reconfiguration, reliance solely on the Lorentz force is insufficient; low thrust is also necessary. To address this, this article proposes an optimal control framework based on reinforcement learning (RL). It derives the nonlinear dynamics of relative motion within the geomagnetic field, considering intersatellite Lorentz force, atmospheric drag, and Earth's gravitational harmonics. The study employs Lagrangian coherent structure analysis to identify relative equilibrium configurations and develops an RL-based optimal control strategy for real-time formation reconfiguration. By leveraging optimal demonstrations, the framework guides the agent's actions to match these demonstrations over time, especially when encountering out-of-distribution states. Numerical simulations confirm the method's optimality, robustness, and real-time performance, highlighting its potential in achieving optimal control and adapting to varying environment in future space missions.
本文介绍了一种利用洛伦兹增强技术进行航天器编队飞行的新方法。研究表明，由洛伦兹力驱动的航天器之间的相对运动具有有利于编队维持的平衡状态。然而，对于有效的编队重新配置，仅依靠洛伦兹力是不够的；低推力也是必要的。为了解决这个问题，本文提出了一种基于强化学习（RL）的最优控制框架。它推导了考虑卫星间洛伦兹力、大气阻力和地球引力谐波的地磁场内相对运动的非线性动力学。该研究采用拉格朗日相干结构分析来识别相对平衡构型，并为实时编队重新配置开发了一种基于RL的最优控制策略。通过利用最优示范，该框架引导智能体的行动随着时间的推移与这些示范相匹配，特别是在遇到分布外状态时。数值模拟证实了该方法的最优性、鲁棒性和实时性能，突出了其在未来太空任务中实现最优控制和适应变化环境的潜力。
