Model reduction for LPV systems based on approximate modal decomposition

Tamás Luspay, Tamás Péni, István Gőzse, Zoltán Szabó, Bálint Vanek

Research output: Contribution to journalArticle

8 Citations (Scopus)


The paper presents a novel model order reduction technique for large-scale linear parameter-varying (LPV) systems. The approach is based on decoupling the original dynamics into smaller dimensional LPV subsystems that can be independently reduced by parameter-varying reduction methods. The decomposition starts with the construction of a modal transformation that separates the modal subsystems. Hierarchical clustering is applied then to collect the dynamically similar modal subsystems into larger groups. The resulting parameter-varying subsystems are then independently reduced. This approach substantially differs from most of the previously proposed LPV model reduction techniques, since it performs manipulations on the LPV model itself, instead of on a set of linear time-invariant models defined at fixed scheduling parameter values. Therefore, the interpolation, which is often a challenging part in reduction techniques, is inherently solved. The applicability of the developed algorithm is thoroughly investigated and demonstrated by numerical case studies.

Original languageEnglish
Pages (from-to)891-909
Number of pages19
JournalInternational Journal for Numerical Methods in Engineering
Issue number6
Publication statusPublished - Feb 10 2018


  • balanced realization
  • clustering
  • linear parameter-varying systems
  • modal transformation
  • model order reduction

ASJC Scopus subject areas

  • Numerical Analysis
  • Engineering(all)
  • Applied Mathematics

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