Using horizon estimation and nonlinear optimization for grey-box identification
2015 (English)In: Journal of Process Control, ISSN 0959-1524, Vol. 30, 69-79 p.Article in journal (Refereed) Published
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.
System identification; State estimation; Parameter estimation; Optimization; Nonlinear systems; Kalman filtering; Moving horizon estimation; Model predictive control
IdentifiersURN: urn:nbn:se:liu:diva-120061DOI: 10.1016/j.jprocont.2014.12.008ISI: 000356196200007OAI: oai:DiVA.org:liu-120061DiVA: diva2:839960
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]2015-07-062015-07-062015-08-19