Identification of low order parameter varying models for large scale systems

S.K. Wattamwar, S. Weiland, A.C.P.M. Backx

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

In this paper we propose a novel procedure for obtaining reduced dimensional models of large scale multi-phase, non-linear, reactive uid ow systems with geometric parameter uncertainty (corrosion). Our approach is based on the combinations of methods of Proper Orthogonal Decomposition (POD), black box System Identi??cation (SID) techniques and nonlinear spline based blending of local black box models to create Reduced Order Linear Parameter Varying (RO-LPV ) model. The proposed method gives computationally very e??cient reduced dimension models for processes with parameter uncertainty. The e??ciency of proposed approach is illustrated on a benchmark problem depicting industrial Glass Manufacturing Process (GMP) with corrosion of refractory materials as a process parameter uncertainty. The results show good performance of the proposed method.
Original languageEnglish
Title of host publicationProceedings of the 15th IFAC Symposium on System Identification, SYSID 2009, July 6-8, 2009, Saint Malo, France
Place of PublicationOxford
PublisherPergamon
Pages1-6
Publication statusPublished - 2009

Fingerprint Dive into the research topics of 'Identification of low order parameter varying models for large scale systems'. Together they form a unique fingerprint.

  • Cite this

    Wattamwar, S. K., Weiland, S., & Backx, A. C. P. M. (2009). Identification of low order parameter varying models for large scale systems. In Proceedings of the 15th IFAC Symposium on System Identification, SYSID 2009, July 6-8, 2009, Saint Malo, France (pp. 1-6). Pergamon.