A grey-box modeling approach for the reduction of nonlinear systems

Reinout Romijn, Leyla Özkan, Siep Weiland, Jobert Ludlage, Wolfgang Marquardt

Research output: Contribution to journalArticleAcademicpeer-review

57 Citations (Scopus)


A novel model reduction methodology is proposed to approximate large-scale nonlinear dynamical systems. The methodology amounts to finding computationally efficient substitute models for the nonlinear subsystems. Model reduction is pursued by viewing the system as a grey-box (or hybrid) model with a mechanistic (white-box) component and an empirical (black-box) component. Before identifying the substitute model, the mechanistic subsystem is reduced by projection using proper orthogonal decomposition. Subsequently, the empirical component is identified by parameter estimation to substitute the nonlinear subsystem. As a consequence, a reduced model with less nonlinear complexity is obtained. An example involving a distributed parameter system is provided.

Original languageEnglish
Pages (from-to)906-914
Number of pages9
JournalJournal of Process Control
Issue number9
Publication statusPublished - 1 Oct 2008


  • Distributed parameter systems
  • Grey-box modeling
  • Hybrid modeling
  • Model reduction
  • Parameter estimation
  • Proper orthogonal decomposition


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