State-Tracking Iterative Learning Control in Frequency Domain Design for Improved Intersample Behavior
State-Tracking Iterative Learning Control in Frequency Domain Design for Improved Intersample Behavior
复制标题
频域设计中的状态跟踪迭代学习控制可改善样本间行为
DOI：
10.1002/rnc.6511
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Tom Oomen
中科院分区：
文献类型：
--
作者：
Wataru Ohnishi;Nard Strijbosch;Tom Oomen
迭代学习控制(ILC)在执行重复任务的系统的采样实例中产生完美的输出跟踪性能。本文的目的是开发一种状态跟踪迭代学习控制的框架，以减轻输出跟踪迭代学习控制中经常遇到的样本间振荡行为。作为分析的框架，推导了迭代域的稳定性，包括鲁棒滤波和渐近信号。此外，作为设计的框架，给出了利用频响数据减少建模工作量的设计方法，基于逆的学习滤波器的设计，以及鲁棒性滤波器的具体设计过程。将所设计的方法成功地应用于运动系统，结果表明，所提出的状态跟踪迭代学习控制比标准输出跟踪迭代学习控制具有更好的样本间行为。
Iterative learning control (ILC) yields perfect output‐tracking performance at sampling instances for systems that perform repetitive tasks. The aim of this article is to develop a framework for a state‐tracking ILC that mitigates oscillatory intersample behavior, which is often encountered in output tracking ILC. As a framework for the analysis, the stability of the iterative domain including the robustness filter and the asymptotic signal is formulated. In addition, as a framework for the design, the design method using frequency response data to reduce the modeling effort, the learning filter design based on inversion, and the specific design procedure of the robustness filter are presented. The designed method is successfully applied to a motion system and it is shown that the presented state‐tracking ILC provides better intersample behavior than the standard output‐tracking ILC.