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.

翻译：姿态控制对诸多卫星任务至关重要。然而，经典控制器设计耗时，且对模型不确定性和运行边界条件变化敏感。深度强化学习通过使控制器在与仿真环境的自主交互中学习自适应控制策略，提供了一种有前景的替代方案。克服仿真到现实差距（即将在仿真环境中训练的智能体部署到真实物理卫星上）仍是一项重大挑战。本文首次成功演示了基于人工智能的姿态控制器在惯性指向机动中的在轨应用。该控制器完全在仿真环境中训练，并部署至InnoCube 3U纳卫星上，该卫星由尤利乌斯-马克西米利安维尔茨堡大学与柏林工业大学合作研制，于2025年1月发射。本文介绍了人工智能智能体的设计、训练方法流程、仿真与真实卫星观测行为之间的差异，以及基于人工智能的姿态控制器与InnoCube经典PD控制器的对比。稳态指标证实了该人工智能控制器在重复在轨机动中的稳健性能。