文章基本信息

标题：Learning Control Without Prior Models: Multi-Variable Model-Free IIC, with application to a Wide-Format Printer ⁎
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作者：Robin de Rozario ; Tom Oomen
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：15
页码：91-96
DOI：10.1016/j.ifacol.2019.11.656
语种：English
出版社：Elsevier
摘要：AbstractLearning control enables performance improvement of mechatronic systems that operate in a repetitive manner. Achieving desirable learning behavior typically requires prior knowledge in the form of a model. The prior modeling requirements can be significantly reduced by using past operational data to estimate this model during the learning process. The aim of this paper is to develop such a data-driven learning control method for multi-variable systems, which requires that directionality aspects are properly addressed. This is achieved by using multiple past experiments to estimate a frequency response function of the inverse dynamics while ensuring smooth convergence by using smoothed pseudo inversion. The developed method is successfully applied to an industrial wide-format printer, resulting in high performance.
关键词：KeywordsFrequency response methodsLinear multivariable systemsSystem identificationConvergence analysisNonlinear analysis