[2509.01388] End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System

Electrical Engineering and Systems Science > Systems and Control arXiv:2509.01388 (eess) [Submitted on 1 Sep 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System Authors:Philipp Hartmann, Jannick Stranghöner, Klaus Neumann View a PDF of the paper titled End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System, by Philipp Hartmann and 2 other authors View PDF HTML (experimental) Abstract:Magnetic levitation is poised to revolutionize industrial automation by integrating flexible in-machine product transport and seamless manipulation. It is expected to become the standard drive technology for automated manufacturing. However, controlling such systems is inherently challenging due to their complex, unstable dynamics. Traditional control approaches, which rely on hand-crafted control engineering, typically yield robust but conservative solutions, with their performance closely tied to the expertise of the engineering team. In contrast, learning-based neural control presents a promising alternative. This paper presents the first neural controller for 6D magnetic levitation. Trained end-to-end on interaction data from a proprietary controller, it directly maps raw sensor data and 6D reference poses to coil current commands. The neural controller can effectively generalize to previously unseen situations while maintaining accurate and robust control. T...