Model-free learning of wire winding control

Abdel Rodríguez, Peter Vrancx, A. Nowé, E. Hostens
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引用次数: 7

Abstract

In this paper we introduce a reinforcement learning approach to optimize the wire profile generated by an automated wire winding machine. The wire winder spools wire onto large bobbins, while trying to maintain an even wire profile across the bobbin. Uneven profiles that contain bumps or gaps (i.e. areas with too much or too little wire) lead to snagged or breaking wires when the bobbin is unwound. By setting the turning points of the traversal system which distributes the wire over a spinning bobbin, a controller can influence the amount of wire spooled on the edges of the bobbin. The behavior of the wire, however, is highly non-deterministic and difficult to model with sufficient accuracy, making the application of a model based controller technique very difficult. This fact makes reinforcement learning a promising approach to apply here, as this technique can learn optimal policies relying only on interactions with the plant. We apply a learning algorithm called continuous reinforcement learning automata and empirically demonstrate that this technique can successfully optimize the wire profile, even on rounded bobbins that require continuous adaptation of the turning point.
无模型学习绕线控制
本文介绍了一种强化学习方法来优化由自动绕线机生成的线材轮廓。绕线机将线轴绕到大的线轴上,同时试图在线轴上保持均匀的线轴轮廓。不均匀的轮廓包含凸起或间隙(即线太多或太少的区域)导致线轴松开时线被卡住或折断。通过设置在纺纱筒上分配导线的穿越系统的转折点,控制器可以影响绕在纺纱筒边缘上的导线数量。然而,电线的行为是高度不确定性的,难以以足够的精度建模,使得基于模型的控制器技术的应用非常困难。这一事实使得强化学习成为一种很有前途的方法,因为这种技术可以仅依靠与植物的相互作用来学习最佳策略。我们应用了一种称为连续强化学习自动机的学习算法,并通过经验证明,这种技术可以成功地优化线材轮廓,即使是在需要连续适应转折点的圆线轴上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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