首个在轨验证的AI卫星姿态控制器，实现自主学习与真实飞行稳定运行。
LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller
- 通过仿真训练深度强化学习算法，实现无需人工调参的自适应控制。
- 在轨多次执行姿态调整任务，稳态性能优于传统PD控制器。
- 为未来智能航天器提供可复制的自主控制范例，适合航天研发团队参考。
姿态控制对众多卫星任务至关重要。传统控制器设计耗时且对模型不确定性及工作边界变化敏感。深度强化学习（DRL）通过与仿真环境自主交互，学习自适应控制策略，是潜在替代方案。然而，克服从仿真到真实的“Sim2Real”差距仍是重大挑战。本文首次实现了基于AI的姿态控制器在轨成功演示，用于惯性指向机动。该控制器完全在仿真中训练，部署于由维尔茨堡大学与柏林工业大学合作研制的InnoCube 3U纳卫星，于2025年1月发射。我们展示了AI智能体设计、训练方法，分析了仿真与真实卫星行为间的差异，并在相同任务条件下对比了该AI控制器与InnoCube原生比例-微分（PD）控制器的性能。稳态指标证实，该AI控制器在多次在轨机动中表现稳健可靠。
原文摘要 · Abstract (English)
Attitude control is essential for many satellite missions. Classical controllers, however, are time-consuming to design and sensitive to model uncertainties and variations in operational boundary conditions. Deep Reinforcement Learning (DRL) offers a promising alternative by learning adaptive control strategies through autonomous interaction with a simulation environment. Overcoming the Sim2Real gap, which involves deploying an agent trained in simulation onto the real physical satellite, remains a significant challenge. In this work, we present the first successful in-orbit demonstration of an AI-based attitude controller for inertial pointing maneuvers. The controller was trained entirely in simulation and deployed to the InnoCube 3U nanosatellite, which was developed by the Julius-Maximilians-Universität Würzburg in cooperation with the Technische Universität Berlin, and launched in January 2025. We present the AI agent design, the methodology of the training procedure, the discrepancies between the simulation and the observed behavior of the real satellite, and a comparison of the AI-based attitude controller with the classical PD controller of InnoCube. Steady-state metrics confirm the robust performance of the AI-based controller during repeated in-orbit maneuvers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。