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Using horizon estimation and nonlinear optimization for grey-box identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Corp Research, Sweden.
Linköping University, Department of Electrical Engineering. Volvo Construct Equipment, Sweden.
SenionLab AB, S-58330 Linkoping, Sweden.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
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2015 (English)In: Journal of Process Control, ISSN 0959-1524, Vol. 30, 69-79 p.Article in journal (Refereed) Published
Abstract [en]

An established method for grey-box identification is to use maximum-likelihood estimation for the nonlinear case implemented via extended Kalman filtering. In applications of (nonlinear) model predictive control a more and more common approach for the state estimation is to use moving horizon estimation, which employs (nonlinear) optimization directly on a model for a whole batch of data. This paper shows that, in the linear case, horizon estimation may also be used for joint parameter estimation and state estimation, as long as a bias correction based on the Kalman filter is included. For the nonlinear case two special cases are presented where the bias correction can be determined without approximation. A procedure how to approximate the bias correction for general nonlinear systems is also outlined. (C) 2015 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Elsevier , 2015. Vol. 30, 69-79 p.
Keyword [en]
System identification; State estimation; Parameter estimation; Optimization; Nonlinear systems; Kalman filtering; Moving horizon estimation; Model predictive control
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-120061DOI: 10.1016/j.jprocont.2014.12.008ISI: 000356196200007OAI: oai:DiVA.org:liu-120061DiVA: diva2:839960
Note

Funding Agencies|Swedish Foundation for Strategic Research (SSF) - as part of the Process Industry Centre Linkoping (PIC-LI); Swedish Agency for Innovation Systems (VINNOVA) through the ITEA 2 project MODRIO; Linnaeus Center CADICS - Swedish Research Council; ERC advanced grant LEARN - European Research Council [similar to267381]

Available from: 2015-07-06 Created: 2015-07-06 Last updated: 2015-08-19

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Isaksson, AlfSjöberg, JohanLjung, LennartKok, Manon
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Automatic ControlFaculty of Science & EngineeringDepartment of Electrical Engineering
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