文章基本信息

标题：Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control
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作者：Qiugang Lu ; Sungho Shin ; Victor M. Zavala 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：11289-11294
DOI：10.1016/j.ifacol.2020.12.373
语种：English
出版社：Elsevier
摘要：AbstractDynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-order models from data. Controller design tasks based on such models require estimates and guarantees on predictive accuracy. In this work, we provide a theoretical analysis of DMD model errors that reveals impacts of model order and data availability. The analysis also establishes conditions under which DMD models can be made asymptotically exact. We numerically validate our theoretical results using a 2D diffusion system.
关键词：KeywordsDynamic mode decompositiondatacontrollow-order modelserror bounds