Tom Baumann

Artificial Intelligence (AI) is rapidly transforming engineering fields, from robotics to aerospace, with applications in control systems for UAVs and satellites. This work builds on a previously developed AI attitude controller for the InnoCube 3U nanosatellite. Deploying complex Neural Networks (NNs) on resource-limited microcontrollers presents a significant challenge. To overcome this, we propose distilling a Multi-Layer Perceptron (MLP) trained with Deep Reinforcement Learning (DRL) for attitude control into a Kolmogorov–Arnold Network (KAN). We convert this numeric KAN into a symbolic KAN, where each edge represents a learnable mathematical function, and finally extract a concise symbolic formula. This symbolic representation dramatically reduces memory usage and computational complexity, making it ideal for pico- and nanosatellites. We evaluate and demonstrate the feasibility of this approach for inertial pointing with reaction wheels in simulation using a realistic model of the InnoCube satellite. Our results show that the highly compressed KANs successfully solve the attitude control problem, while reducing the required memory footprint and inference time on the InnoCube ADCS hardware by over an order of magnitude. Beyond attitude control, we believe symbolic KANs hold great potential in aerospace for neural network compression and interpretable, data-driven modeling and system identification in future space missions.

A new innovative satellite mission, the Innovative CubeSat for Education (InnoCube), is addressed. The goal of the mission is to demonstrate “the wireless satellite”, which replaces the data harness by robust, high-speed, real-time, very short-range radio communications using the SKITH (SKIpTheHarness) technology. This will make InnoCube the first wireless satellite in history. Another technology demonstration is an experimental energy-storing satellite structure that was developed in the previous Wall#E project and might replace conventional battery technology in the future. As a further payload, the hardware for the concept of a software-based solution for receiving signals from Global Navigation Satellite Systems (GNSS) will be developed to enable precise position determination of the CubeSat. Aside from technical goals this work aims to be of use in the teaching of engineering skills and practical sustainable education of students, important technical and scientific publications, and the increase of university skills. This article gives an overview of the overall design of the InnoCube.