LeLaR: The First In-Orbit Demonstration of an AI-Based Satellite Attitude Controller

2025-12-22

AI Summary

Traditional satellite attitude controllers suffer from insufficient robustness against model uncertainties and operational condition variations, along with lengthy design cycles. To address these challenges, this work proposes an end-to-end adaptive control method based on deep reinforcement learning (DRL). A Proximal Policy Optimization (PPO) agent is trained in a high-fidelity dynamics simulator and subsequently optimized for embedded AI inference. For the first time, a purely simulation-trained DRL attitude controller is directly deployed onboard the InnoCube 3U nanosatellite—successfully bridging the sim-to-real gap. For inertial-pointing missions, the controller achieves an in-orbit steady-state pointing accuracy of 0.1° during repeated maneuvers, reduces overshoot by 40%, and demonstrates significantly enhanced disturbance rejection compared to conventional PD controllers. These results validate the reliability and superior performance of AI-based controllers in the real space environment.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Develops an AI-based satellite attitude controller for inertial pointing maneuvers
• Addresses the Sim2Real gap by deploying a simulation-trained agent to a real satellite
• Compares the AI controller's performance with a classical PD controller in orbit

Innovation

• AI-based controller trained entirely in simulation
• First in-orbit demonstration on a nanosatellite
• Overcame Sim2Real gap for adaptive attitude control