论文标题
加强学习方法的动态进动驱动系统振动补偿
Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems
论文作者
论文摘要
振动补偿对于许多领域都很重要。对于机床行业，它转化为更高的加工精度和较长的组件寿命。当前的振动阻尼方法具有其缺点（例如，需要精确的动态模型）。在本文中，我们提出了一种基于加强学习的方法，用于应用于机床轴的振动补偿。这项工作描述了使用工业机床硬件和控制系统的问题制定，解决方案，实现和实验。
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.
