文章基本信息

标题：Higher-order Iterative Learning Control Law Design using Linear Repetitive Process Theory: Convergence and Robustness
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作者：Xuan Wang ; Bing Chu ; Eric Rogers 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：3123-3128
DOI：10.1016/j.ifacol.2017.08.320
语种：English
出版社：Elsevier
摘要：AbstractIterative learning control has been developed for processes or systems that complete the same finite duration task over and over again. The mode of operation is that after each execution is complete the system resets to the starting location, the next execution is completed and so on. Each execution is known as a trial and its duration is termed the trial length. Once each trial is complete the information generated is available for use in computing the control input for the next trial. This paper uses the repetitive process setting to develop new results on the design of higher-order ILC control laws for discrete dynamics. The new results include conditions that guarantee error convergence and design in the presence of model uncertainty.
关键词：Keywordsiterative learning controlhigher-order laws2D systems