Title: End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System Show Chinese title

Title: 端到端低级神经控制工业级六维磁悬浮系统

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 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, neural control learning 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. These results underscore the practical feasibility of learning-based neural control in complex physical systems and suggest a future where such a paradigm could enhance or even substitute traditional engineering approaches in demanding real-world applications. The trained neural controller, source code, and demonstration videos are publicly available at https://sites.google.com/view/neural-maglev. Show Chinese abstract

Abstract: 磁悬浮技术有望通过集成灵活的机器内产品运输和无缝操作来革新工业自动化。预计将成为自动化制造的标准驱动方式。然而，由于其复杂的不稳定性动态，控制此类系统本质上具有挑战性。传统的控制方法依赖于手工设计的控制工程，通常产生稳健但保守的解决方案，其性能与工程团队的专业知识密切相关。相比之下，神经控制学习提供了一个有前景的替代方案。本文提出了第一个用于6D磁悬浮的神经控制器。它在专有控制器的交互数据上端到端训练，直接将原始传感器数据和6D参考位姿映射到线圈电流命令。神经控制器能够在保持精确和鲁棒控制的同时，有效推广到以前未见过的情况。这些结果强调了基于学习的神经控制在复杂物理系统中的实际可行性，并表明在未来，这种范式可能在苛刻的现实应用中增强甚至替代传统的工程方法。训练好的神经控制器、源代码和演示视频可在 https://sites.google.com/view/neural-maglev 公开获取。