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

1 Citation (Scopus)

Abstract

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
Volume40
Issue number5
DOIs
Publication statusPublished - 1 Jan 2007
Event8th IFAC Symposium on Dynamics and Control of Process Systems, 2007 - Cancun, Mexico
Duration: 6 Jun 20168 Jun 2016

Keywords

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

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