论文标题
学习高级运动控制
Learning for Advanced Motion Control
论文作者
论文摘要
迭代学习控制（ILC）可以为机电系统实现完美的跟踪性能。本文的目的是介绍用于工业机电系统系统的ILC设计教程。首先，初步分析揭示了ILC实际实施之前的潜在绩效提高。其次，提出了一种频域方法，其中通过非因果模型倒置实现快速学习，并且通过使用非参数频率响应函数结合使用收缩映射定理来实现安全和健壮的学习。该方法在桌面打印机上显示。最后，对工业运动系统的详细分析导致了一些缺陷，这些缺点阻碍了ILC算法的广泛实施。概述了最近开发的算法，包括使用机器学习算法的扩展，旨在促进广泛的工业部署。
Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.
