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Grey-Box Modeling: Branches and Experiences
KTH, School of Electrical Engineering (EES), Automatic Control.
2008 (English)In: 17th World Congress, International Federation of Automatic Control, IFAC, 2008Conference paper, Published paper (Refereed)
Abstract [en]

This paper deals with methods and experiences of incorporating a priori knowledge into mathematical models of industrial processes and systems. Grey box modelling has been developed in several directions and can be grouped into branches depending on the way a priori knowledge is handled. In this paper we divide grey box modelling into the following branches; constrained black box identification, semi-physical modelling, mechanistic modelling, hybrid modelling and distributed parameter modelling. Experiences from case studies demonstrate the different branches of grey box modelling procedures. In the applications, the grey box models have been used for model based control, soft sensors, process supervision and failure detection. Further, distributed parameter modelling presents a specific challenge in that it is difficult to distinguish model reduction errors from model-data discrepancies. By estimating the model reduction error and forming hypothesis tests based on the estimate, the problem can be dealt with effectively.

Place, publisher, year, edition, pages
2008.
Series
IFAC Proceedings Volumes, ISSN 1474-6670 ; 17
Keyword [en]
Grey box modelling, Identification for control, Nonlinear system identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-89050OAI: oai:DiVA.org:kth-89050DiVA: diva2:502616
Conference
17th World Congress, International Federation of Automatic Control, IFAC. Seoul. 6 July 2008 - 11 July 2008
Note

QC 20120215

Available from: 2012-02-14 Created: 2012-02-14 Last updated: 2017-06-15Bibliographically approved

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