Grey-Box Identification Based on Horizon Estimation and Nonlinear Optimization
2010 (English)Report (Other academic)
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 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. A procedure how to approximate the bias correction for nonlinear systems is outlined.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. , 9 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2963
Grey-box, Identification, Estimation, Nonlinear, Optimization
IdentifiersURN: urn:nbn:se:liu:diva-97605ISRN: LiTH-ISY-R-2963OAI: oai:DiVA.org:liu-97605DiVA: diva2:649237