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Constrained nonlinear state estimation using ensemble kalman filters / J. Prakash in Industrial & engineering chemistry research, Vol. 49 N° 5 (Mars 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 5 (Mars 2010) . - pp. 2242–2253 Titre : Constrained nonlinear state estimation using ensemble kalman filters Type de document : texte imprimé Auteurs : J. Prakash, Auteur ; Sachin C. Patwardhan, Auteur ; Shah, Sirish L., Auteur Année de publication : 2010 Article en page(s) : pp. 2242–2253 Note générale : Industrial Chemistry Langues : Anglais (eng) Mots-clés : EnKF;  kalman Résumé : Recursive estimation of states of constrained nonlinear dynamic 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 (EnKF) that retains the advantages of the unconstrained EnKF while systematically dealing with bounds on the estimated states. The EnKF belongs to the class of particle filters that are increasingly being used for solving state estimation problems associated with nonlinear systems. A highlight of our approach is the use of truncated multivariate distributions for systematically solving the estimation problem in the presence of state constraints. The efficacy of the proposed constrained state estimation algorithm using the EnKF is illustrated by application on two benchmark problems in the literature (a simulated gas-phase reactor and an isothermal batch reactor) involving constraints on estimated state variables and another example problem, which involves constraints on the process noise. Note de contenu : Bibliogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900197s [article] Constrained nonlinear state estimation using ensemble kalman filters [texte imprimé] / J. Prakash, Auteur ; Sachin C. Patwardhan, Auteur ; Shah, Sirish L., Auteur . - 2010 . - pp. 2242–2253. Industrial Chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 5 (Mars 2010) . - pp. 2242–2253 Mots-clés : EnKF;  kalman Résumé : Recursive estimation of states of constrained nonlinear dynamic 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 (EnKF) that retains the advantages of the unconstrained EnKF while systematically dealing with bounds on the estimated states. The EnKF belongs to the class of particle filters that are increasingly being used for solving state estimation problems associated with nonlinear systems. A highlight of our approach is the use of truncated multivariate distributions for systematically solving the estimation problem in the presence of state constraints. The efficacy of the proposed constrained state estimation algorithm using the EnKF is illustrated by application on two benchmark problems in the literature (a simulated gas-phase reactor and an isothermal batch reactor) involving constraints on estimated state variables and another example problem, which involves constraints on the process noise. Note de contenu : Bibliogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900197s