NCS: Moving Horizon Estimation: Difference between revisions
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This is the template for CDS 270 lectures. If you edit this page, you will see comments describing what goes in each section. '''Do not edit this template.''' See [[CDS 270: Information for Lecturers]] for more information on how to create a wiki page corresponding to a lecture. --> | This is the template for CDS 270 lectures. If you edit this page, you will see comments describing what goes in each section. '''Do not edit this template.''' See [[CDS 270: Information for Lecturers]] for more information on how to create a wiki page corresponding to a lecture. --> | ||
In this lecture, we give an introduction to moving horizon estimation (MHE) and extended Kalman filters (EKF). These filter stuctures can be used with nonlinear models and are therefore more general than the standard Kalman filter. Furthermore, MHE can also take constraints on the noise and the state space, as well as asymmetric probability distributions, into account. MHE is dual to | In this lecture, we give an introduction to moving horizon estimation (MHE) and extended Kalman filters (EKF). These filter stuctures can be used with nonlinear models and are therefore more general than the standard Kalman filter. Furthermore, MHE can also take constraints on the noise and the state space, as well as asymmetric probability distributions, into account. MHE is dual to receding horizon control (RHC) and also relies on optimization software. The lecture ends with a brief discussion on stability properties of MHE. | ||
== Lecture Materials == | == Lecture Materials == | ||
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== Reading == | == Reading == | ||
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* <p>[http://pubs.acs.org/cgi-bin/abstract.cgi/iecred/2005/44/i08/abs/ie034308l.html Critical evaluation of extended Kalman filtering and moving horizon estimation], E.L. Haseltine and J.B. Rawlings, ''Ind. Eng. Chem. Res.'', vol. 44, no.8, 2005. Contains several examples where EKF and MHE have been applied. Discusses the differences. | |||
* <p>[http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf An Introduction to the Kalman Filter], G. Welch and G. Bishop. Gives a brief introduction to the extended Kalman filter in discrete time.</p> | |||
== Additional Resources == | == Additional Resources == | ||
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Revision as of 17:09, 19 April 2006
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In this lecture, we give an introduction to moving horizon estimation (MHE) and extended Kalman filters (EKF). These filter stuctures can be used with nonlinear models and are therefore more general than the standard Kalman filter. Furthermore, MHE can also take constraints on the noise and the state space, as well as asymmetric probability distributions, into account. MHE is dual to receding horizon control (RHC) and also relies on optimization software. The lecture ends with a brief discussion on stability properties of MHE.
Lecture Materials
Reading
Critical evaluation of extended Kalman filtering and moving horizon estimation, E.L. Haseltine and J.B. Rawlings, Ind. Eng. Chem. Res., vol. 44, no.8, 2005. Contains several examples where EKF and MHE have been applied. Discusses the differences.
An Introduction to the Kalman Filter, G. Welch and G. Bishop. Gives a brief introduction to the extended Kalman filter in discrete time.
