文章基本信息

标题：Overcoming Output Constraints in Iterative Learning Control Systems by Reference Adaptation
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作者：Michael Meindl ; Fabio Molinari ; Jörg Raisch 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：1480-1486
DOI：10.1016/j.ifacol.2020.12.1938
语种：English
出版社：Elsevier
摘要：AbstractIterative Learning Control (ILC) schemes can guarantee properties such as asymptotic stability and monotonic error convergence, but do not, in general, ensure adherence to output constraints. The topic of this paper is the design of a reference-adapting ILC (RAILC) scheme, extending an existing ILC system and capable of complying with output constraints. The underlying idea is to scale the reference at every trial by using a conservative estimate of the output’s progression. Properties as the monotonic convergence above a threshold and the respect of output constraints are formally proven. Numerical simulations and experimental results reinforce our theoretical results.
关键词：KeywordsIterativeRepetitive learning controlLinear systemsLearning for controlControl of constrained systems