CubeSats offer a cost-effective platform for various space missions, but their limited fuel capacity and susceptibility to environmental disturbances pose significant challenges for precise orbital maneuvering. This paper presents a novel control strategy that integrates a J2-optimized sequence with an LSTM-based low-level control layer to address these issues. The J2-optimized sequence leverages the Earth's oblateness to minimize fuel consumption during orbital corrections, while the LSTM network provides real-time adjustments to compensate for external disturbances and unmodeled dynamics. The LSTM network was trained on a dataset generated from simulated orbital scenarios, including factors such as atmospheric drag, solar radiation pressure, and gravitational perturbations. The proposed system was evaluated through numerical simulations, demonstrating significant improvements in maneuver accuracy and robustness compared to traditional methods. The results show that the combined system efficiently reduces miss distances, even under conditions of high uncertainty. This hybrid approach offers a powerful and adaptive solution for CubeSat missions, balancing fuel efficiency with precise orbital control.

翻译：立方星为各类空间任务提供了经济高效的平台，但其有限的燃料容量和对环境扰动的敏感性给精确轨道机动带来了重大挑战。本文提出一种新颖的控制策略，将J2优化序列与基于LSTM的低层控制层相结合以应对这些问题。J2优化序列利用地球扁率最小化轨道修正期间的燃料消耗，而LSTM网络则提供实时调整以补偿外部扰动和未建模动力学。LSTM网络在模拟轨道场景生成的数据集上进行训练，训练数据包含大气阻力、太阳辐射压和引力摄动等因素。通过数值仿真对所提系统进行评估，结果表明与传统方法相比，该系统在机动精度和鲁棒性方面均有显著提升。实验证明该组合系统即使在高度不确定条件下也能有效减少脱靶距离。这种混合方法为立方星任务提供了一种强大且自适应的解决方案，在燃料效率与精确轨道控制之间实现了良好平衡。