文章基本信息

标题：From Nonlinear Identification to Linear Parameter Varying Models: Benchmark Examples ⁎
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作者：Maarten Schoukens ; Roland Tóth
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：419-424
DOI：10.1016/j.ifacol.2018.09.181
语种：English
出版社：Elsevier
摘要：AbstractLinear parameter-varying (LPV) models form a powerful model class to analyze and control a (nonlinear) system of interest. Identifying a LPV model of a nonlinear system can be challenging due to the difficulty of selecting the scheduling variable(s) a priori, which is quite challenging in case a first principles based understanding of the system is unavailable. This paper presents a systematic LPV embedding approach starting from nonlinear fractional representation models. A nonlinear system is identified first using a nonlinear block-oriented linear fractional representation (LFR) model. This nonlinear LFR model class is embedded into the LPV model class by factorization of the static nonlinear block present in the model. As a result of the factorization a LPV-LFR or a LPV state-space model with an affine dependency results. This approach facilitates the selection of the scheduling variable from a data-driven perspective. Furthermore the estimation is not affected by measurement noise on the scheduling variables, which is often left untreated by LPV model identification methods. The proposed approach is illustrated on two well-established nonlinear modeling benchmark examples.
关键词：KeywordsNonlinear SystemsLinear-Parameter Varying SystemsSystem IdentificationEmbeddingLinear Fractional Representation