Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems

Autor:	Gulde, Ralf, Tuscher, Marc, Csiszar, Akos, Riedel, Oliver, Verl, Alexander
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
DOI:	10.1109/AI4I46381.2019.00015
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2004.09263 Zobrazit plný text záznamu View this record from Arxiv