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arXiv · 2004.09263

Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems

Ralf Gulde·Marc Tuscher·Akos Csiszar·Oliver Riedel·Alexander Verl
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Abstract

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.

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Ralf Gulde, Marc Tuscher, Akos Csiszar, Oliver Riedel, Alexander Verl. 2020-04-14. Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems. https://doi.org/10.1109/ai4i46381.2019.00015

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