DocumentCode
2389874
Title
Constrained state estimation using the ensemble Kalman filter
Author
Prakash, J. ; Patwardhan, Sachin C. ; Shah, Sirish L.
Author_Institution
Dept. of Instrum. Eng. MIT Campus, Anna Univ., Chennai
fYear
2008
fDate
11-13 June 2008
Firstpage
3542
Lastpage
3547
Abstract
Recursive estimation of constrained nonlinear dynamical systems has attracted the attention of many researchers in recent years. In this work, we propose a constrained recursive formulation of the ensemble Kalman filter (CEnKF) that retains the advantages of unconstrained Ensemble Kalman Filter while systematically dealing with bounds on the estimated states. The efficacy of the proposed constrained state estimation algorithm using the EnKF is illustrated by application on a simulated gas-phase reactor problem.
Keywords
Kalman filters; nonlinear dynamical systems; recursive estimation; state estimation; CEnKF; constrained state estimation; ensemble Kalman filter; gas-phase reactor problem; nonlinear dynamical systems; recursive estimation; Bayesian methods; Control systems; Filtering; Inductors; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Particle filters; Recursive estimation; State estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 2008
Conference_Location
Seattle, WA
ISSN
0743-1619
Print_ISBN
978-1-4244-2078-0
Electronic_ISBN
0743-1619
Type
conf
DOI
10.1109/ACC.2008.4587042
Filename
4587042
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