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 journalConference articlepeer-review

2 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 an uncertain nonlinear system. Model uncertainty is incorporated by viewing the system as a grey-box or hybrid model with a mechanistic (first-principle) component and an empirical (black-box) component. The mechanistic part is approximated using proper orthogonal decomposition. Subsequently, the empirical part is identified by parameter estimation using the reduced order mechanistic part. As a consequence, the parameter estimation is computationally more efficient. An example with a distributed parameter system is provided.

Original languageEnglish
Pages (from-to)239-244
Number of pages6
JournalIFAC Proceedings Volumes
Issue number5
Publication statusPublished - 1 Jan 2007
Event8th IFAC International Symposium on Dynamics and Control of Process Systems, DYCOPS 2007 - Cancun, Mexico
Duration: 4 Jun 20076 Jun 2007
Conference number: 8


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


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