An Upper Limit for Iterative Learning Control Initial Input Construction Using Singular Values

Naser Alajmi, Ali Alobaidly, Mubarak K. Alhajri, Salem H. Salamah, M. A. Alsubaie
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引用次数: 1

Abstract

Selecting a proper initial input for Iterative Learning Control (ILC) algorithms has been shown to offer faster learning speed compared to the same theories if a system starts from blind. Iterative Learning Control is a control technique that uses previous successive projections to update the following execution/trial input such that a reference is followed to a high precision. In ILC, convergence of the error is generally highly dependent on the initial choice of input applied to the plant, thus a good choice of initial start would make learning faster and as a consequence the error tends to zero faster as well. Here in this paper, an upper limit to the initial choice construction for the input signal for trial 1 is set such that the system would not tend to respond aggressively due to the uncertainty that lies in high frequencies. The provided limit is found in term of singular values and simulation results obtained illustrate the theory behind.
奇异值迭代学习控制初始输入构造的一个上限
与相同的理论相比,如果系统从盲开始,为迭代学习控制(ILC)算法选择合适的初始输入可以提供更快的学习速度。迭代学习控制是一种控制技术,它使用以前的连续投影来更新下面的执行/试验输入,从而使参考达到高精度。在ILC中,误差的收敛性通常高度依赖于应用于对象的输入的初始选择,因此,良好的初始启动选择将使学习更快,因此误差也趋向于更快地归零。在本文中,试验1的输入信号的初始选择结构的上限被设置为使得系统不会由于高频中的不确定性而倾向于积极响应。所提供的极限是根据奇异值找到的,所获得的模拟结果说明了背后的理论。
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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