标题： 基于未知扰动的卫星姿态控制模仿学习 显示英文标题

标题： Imitation Learning for Satellite Attitude Control under Unknown Perturbations

摘要： 本文提出了一种新颖的卫星姿态控制框架，该框架将软演员-评论家（SAC）强化学习与生成对抗模仿学习（GAIL）相结合，以在各种未知扰动下实现鲁棒性能。传统控制技术通常依赖于精确的系统模型，并且对参数不确定性和外部扰动敏感。为了克服这些限制，我们首先开发了一个基于SAC的专家控制器，该控制器在应对执行器故障、传感器噪声和姿态不对齐方面表现出更强的弹性，在几个具有挑战性的场景中优于我们之前的结果。然后，我们使用GAIL训练一个模仿专家轨迹的学习策略，从而通过专家演示减少训练成本并提高泛化能力。在单个和组合扰动下的初步实验表明，SAC专家可以将天线旋转到指定方向，并在大多数列出的扰动中可靠地保持天线方向稳定。此外，GAIL学习者可以模仿SAC专家生成轨迹中的大部分特征。比较评估和消融研究证实了SAC算法和奖励塑造的有效性。GAIL的集成进一步降低了样本复杂度，并展示了有前景的模仿能力，为更智能和自主的航天器控制系统铺平了道路。 显示英文摘要

摘要： This paper presents a novel satellite attitude control framework that integrates Soft Actor-Critic (SAC) reinforcement learning with Generative Adversarial Imitation Learning (GAIL) to achieve robust performance under various unknown perturbations. Traditional control techniques often rely on precise system models and are sensitive to parameter uncertainties and external perturbations. To overcome these limitations, we first develop a SAC-based expert controller that demonstrates improved resilience against actuator failures, sensor noise, and attitude misalignments, outperforming our previous results in several challenging scenarios. We then use GAIL to train a learner policy that imitates the expert's trajectories, thereby reducing training costs and improving generalization through expert demonstrations. Preliminary experiments under single and combined perturbations show that the SAC expert can rotate the antenna to a specified direction and keep the antenna orientation reliably stable in most of the listed perturbations. Additionally, the GAIL learner can imitate most of the features from the trajectories generated by the SAC expert. Comparative evaluations and ablation studies confirm the effectiveness of the SAC algorithm and reward shaping. The integration of GAIL further reduces sample complexity and demonstrates promising imitation capabilities, paving the way for more intelligent and autonomous spacecraft control systems.