LeLaR：首个基于AI的卫星姿态控制器在轨演示
LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller
AI总结：
本文提出首个在轨演示的基于AI的卫星姿态控制器，通过仿真训练部署至InnoCube 3U卫星，验证了AI控制器在重复姿态 maneuver 中的鲁棒性，对比传统PD控制器表现优异。
摘要：
姿态控制对许多卫星任务至关重要。然而，传统控制器设计耗时且对模型不确定性和操作边界条件变化敏感。深度强化学习（DRL）通过自主交互仿真环境学习自适应控制策略，成为有前景的替代方案。克服Sim2Real鸿沟，即在真实物理卫星上部署仿真训练的智能体，仍是一个重大挑战。本文首次在轨演示基于AI的惯性指向 maneuver 控制器。控制器完全在仿真中训练，并部署到由乌尔姆大学与柏林技术大学合作开发的InnoCube 3U微型卫星，于2025年1月发射。我们展示了AI智能体设计、训练方法、仿真与真实卫星行为差异，以及AI控制器与InnoCube传统PD控制器的对比。稳态指标证实了AI控制器在重复在轨 maneuver 中的鲁棒性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
