用状态跟踪迭代学习控制改进非最小相位系统的样本间行为

Liang Oei, Kentaro Tsurumoto, W. Ohnishi
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引用次数: 0

摘要

迭代学习控制是一种成熟的技术,可以实现对重复性运动任务的完美跟踪。然而,传统的输出跟踪ILC侧重于完美的样本跟踪,而采样实例之间经常发生振荡。本文的目的是为了减少最小和非最小相位系统的跟踪控制中的采样间振荡。将一种新的ILC框架——状态跟踪ILC成功地应用于运动系统。与输出跟踪ILC相比,状态跟踪ILC实现了完美的状态跟踪,并减少了四阶最小和非最小相位运动系统的采样间振荡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Intersample Behaviour of Non-Minimum Phase Systems using State-Tracking Iterative Learning Control
Iterative learning control is well-proven technique to achieve perfect tracking performance for repetitive motion tasks. However, traditional output-tracking ILC focuses on perfect on-sample tracking, while oscillations often occur between the sampling instances. The aim of this paper is to reduce the intersample oscillations for the tracking control of minimum and non-minimum phase systems. A new ILC framework called statetracking ILC is successfully applied to a motion system. The state-tracking ILC achieves perfect state-tracking and is shown to reduce the intersample oscillations on fourth-order minimum and non-minimum phase motion systems compared to the outputtracking ILC.
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