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<p>Advancements in satellite technology have enabled the deployment of large constellations in low Earth orbit (LEO), presenting significant challenges in orbital management. Operating hundreds or even thousands of satellites imposes considerable computational and communication demands on ground control systems and operators. A primary operational challenge is maintaining satellites on their nominal trajectories in the presence of continuous perturbations, such as atmospheric drag and third-body gravitational forces. This article presents a reinforcement learning (RL)-based approach for decentralized satellite station keeping (SK). The proposed method utilizes a neural network policy—trained with the model-free soft actor-critic (SAC) algorithm in a high-fidelity, physics-based simulation environment—to compute corrective thrust vectors based on observed deviations from the satellite’s nominal unperturbed trajectory. The resulting policy is computationally efficient and suitable for deployment onboard resource-constrained, space-grade systems. The performance of the RL-based controller is evaluated through Monte Carlo simulations and compared with that of a conventional linear model predictive controller (MPC), which is widely adopted due to its relatively low computational requirements. The comparison focuses on computational complexity, control performance, and robustness. Results demonstrate that the RL-based controller can achieve improved maneuver efficiency by directly learning the nonlinear relative dynamics, without incurring the computational cost typically associated with classical nonlinear optimization-based control methods. These findings underscore the potential of RL techniques for scalable and autonomous management of satellite constellations.</p>

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Reinforcement Learning Based Intelligent Control for Low-thrust Tight Station Keeping in Low Earth Orbit

  • 期刊论文

  • 发表日期：

    2025年7月14日

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  • Nektarios Aristeidis Tafanidis,
  • Avijit Banerjee,
  • Sumeet Gajanan Satpute,
  • George Nikolakopoulos

摘要

Advancements in satellite technology have enabled the deployment of large constellations in low Earth orbit (LEO), presenting significant challenges in orbital management. Operating hundreds or even thousands of satellites imposes considerable computational and communication demands on ground control systems and operators. A primary operational challenge is maintaining satellites on their nominal trajectories in the presence of continuous perturbations, such as atmospheric drag and third-body gravitational forces. This article presents a reinforcement learning (RL)-based approach for decentralized satellite station keeping (SK). The proposed method utilizes a neural network policy—trained with the model-free soft actor-critic (SAC) algorithm in a high-fidelity, physics-based simulation environment—to compute corrective thrust vectors based on observed deviations from the satellite’s nominal unperturbed trajectory. The resulting policy is computationally efficient and suitable for deployment onboard resource-constrained, space-grade systems. The performance of the RL-based controller is evaluated through Monte Carlo simulations and compared with that of a conventional linear model predictive controller (MPC), which is widely adopted due to its relatively low computational requirements. The comparison focuses on computational complexity, control performance, and robustness. Results demonstrate that the RL-based controller can achieve improved maneuver efficiency by directly learning the nonlinear relative dynamics, without incurring the computational cost typically associated with classical nonlinear optimization-based control methods. These findings underscore the potential of RL techniques for scalable and autonomous management of satellite constellations.