动态进给驱动系统振动补偿的再励学习方法
Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems
摘要：
振动补偿对于许多领域都是重要的。对于机床行业来说，它意味着更高的加工精度和更长的部件寿命。目前用于振动阻尼的方法具有其缺点（例如，需要精确的动态模型）。在本文中，我们提出了一种基于强化学习的方法，应用于机床轴的振动补偿。这项工作描述了问题的制定，解决方案，实施和实验使用工业机床硬件和控制系统。
英文摘要：
Vibration compensation is important for many domains. For the machine tool industry it translates to higher machining precision and longer component lifetime. Current methods for vibration damping have their shortcomings (e.g. need for accurate dynamic models). In this paper we present a reinforcement learning based approach to vibration compensation applied to a machine tool axis. The work describes the problem formulation, the solution, the implementation and experiments using industrial machine tool hardware and control system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
