Learning-Based Reconfiguration of Charged Spacecraft Formation in Geomagnetic Field.

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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.