文章基本信息

标题：Partial Fraction Expansion Based Frequency Weighted Balanced Singular Perturbation Approximation Model Reduction Technique with Error Bounds
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作者：Deepak Kumar ; Ahmad Jazlan ; Victor Sreeram 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：9
页码：45-50
DOI：10.1016/j.ifacol.2016.07.488
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, a new frequency weighted partial fraction expansion based model reduction technique is developed based on the partial fraction expansion approach. In order to further reduce the frequency weighted approximation error, singular perturbation approximation is incorporated into the algorithm. This technique results in stable reduced order models regardless if single sided or double sided weights are used. Error bounds are also derived for the proposed method. For minimization of the frequency weighted approximation error, free parameters are introduced into the algorithm. A numerical example is provided in order to validate the proposed algorithm.
关键词：KeywordsModel ReductionFrequency WeightingSingular Perturbation ApproximationPartial Fraction ExpansionSquare Root Balancing