文章基本信息

标题：Generalization of Tensor Product Model Transformation for Control Design
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作者：József Kuti ; Péter Galambos ; Péter Baranyi 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：5604-5609
DOI：10.1016/j.ifacol.2017.08.1106
语种：English
出版社：Elsevier
摘要：AbstractThe paper shows that the separated structure of parameter dependencies within the Polytopic Tensor-Product (TP) model can be exploited during the controller design by applying controller candidates that depend only on certain parameter sets. This approach combines the polytopic uncertainty and Parallel Distributed Compensation (PDC) concepts in such a way that they become special cases of the proposed formulation. Motivated by this recognition and the fact that the separation of parameter dependencies increases the complexity and computational cost, the definition of Polytopic TP representation for LPV/qLPV models is relaxed. The new definition allows for the use of arbitrarily chosen parameter sets in the model - according to the control goals - and the separation can be performed only for these sets. Through the derivation, an appropriate transformation algorithm and the unique Affine TP model is introduced discussing the related concepts of tensor algebra and affine geometry as well.
关键词：KeywordsGain shedulingLPV methodologiescontroller constraintsstructureTP model tranformationPolytopic model based controlPolytopic TP model based control