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Nonlinear system identification with applications to selective catalytic reduction systems
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The stringent regulations on the emissions levels of heavy duty vehicles create a demand for new methods of reducing harmful emissions from the engine. In order to be able to follow these increasingly stricter legislations, complex aftertreatment systems are used. Achievement of optimal performance of these systems requires accurate models that can be used for control design. As a result, the interest in modelling and control of aftertreatment systems has increased.

This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using the selective catalyst as an aftertreatment system for its reduction. The process of the selective catalytic reduction (SCR) is nonlinear since the chemical reactions involved are highly depending on the operating point. The momentary operating point is defined by the driving profile of the vehicle which, for example, includes cold and hot engine starts, highway and urban driving.

The purpose of this thesis is to investigate different methods for nonlinear system identification of SCR systems with control in mind. The first two papers contain the theoretical work of this thesis. The first paper deals with improvement of an existing recursive prediction error method (RPEM) where a more accurate discretisation algorithm was used to improve the accuracy of the estimated nonlinear model. The second paper deals with analysis of the convergence properties of the algorithm. For this analysis several conditions were formulated that link the global and local convergence properties of the algorithm to stability properties of an associated differential equation. Global convergence to a stationary point was shown. In the third paper, the RPEM is used for identification of the SCR system and finally the fourth paper a Hammerstein–Wiener model for identification of the SCR system is applied. In both these cases the black-box models could predict the NOx behaviour of the SCR system quite well. The nonlinear models were shown to describe the SCR system more accurately than linear models.

Place, publisher, year, edition, pages
Uppsala University, 2012.
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2012-004
National Category
Control Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
URN: urn:nbn:se:uu:diva-186963OAI: oai:DiVA.org:uu-186963DiVA: diva2:573410
Supervisors
Available from: 2012-05-31 Created: 2012-11-30 Last updated: 2017-08-31Bibliographically approved
List of papers
1. Recursive Identification and Scaling of Non-linear Systems using Midpoint Numerical Integration
Open this publication in new window or tab >>Recursive Identification and Scaling of Non-linear Systems using Midpoint Numerical Integration
2010 (English)Report (Other academic)
Series
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2010-025
National Category
Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-138756 (URN)
Available from: 2010-10-26 Created: 2010-12-19 Last updated: 2012-11-30Bibliographically approved
2. Convergence analysis and experiments using an RPEM based on nonlinear ODEs and midpoint integration
Open this publication in new window or tab >>Convergence analysis and experiments using an RPEM based on nonlinear ODEs and midpoint integration
2012 (English)In: Proc. 51st Conference on Decision and Control, Piscatway, NJ: IEEE, 2012, 2858-2865 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscatway, NJ: IEEE, 2012
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-186961 (URN)10.1109/CDC.2012.6426545 (DOI)000327200403036 ()978-1-4673-2065-8 (ISBN)
Conference
CDC 2012, December 10–13, Maui, HI
Available from: 2013-02-04 Created: 2012-11-30 Last updated: 2017-08-10Bibliographically approved
3. Nonlinear black box identification of a selective catalytic reduction system
Open this publication in new window or tab >>Nonlinear black box identification of a selective catalytic reduction system
2011 (English)In: Proc. 18th IFAC World Congress, International Federation of Automatic Control , 2011, 11845-11850 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
International Federation of Automatic Control, 2011
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-163088 (URN)978-3-902661-93-7 (ISBN)
Available from: 2011-12-07 Created: 2011-12-07 Last updated: 2012-11-30Bibliographically approved
4. Identification of a discrete-time nonlinear Hammerstein-Wiener model for a selective catalytic reduction system
Open this publication in new window or tab >>Identification of a discrete-time nonlinear Hammerstein-Wiener model for a selective catalytic reduction system
2011 (English)In: Proc. American Control Conference: ACC 2011, American Automatic Control Council , 2011, 78-83 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
American Automatic Control Council, 2011
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-157960 (URN)000295376000014 ()978-1-4577-0080-4 (ISBN)
Available from: 2011-08-18 Created: 2011-08-27 Last updated: 2012-11-30Bibliographically approved

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Citation style
  • apa
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  • Other locale
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Output format
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