DSpace
 

DSpace at IIT Bombay >
IITB Publications >
Article >

Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/3767

Title: Constrained Nonlinear State Estimation Using Ensemble Kalman Filters
Authors: PRAKASH, J
PATWARDHAN, SC
SHAH, SL
Keywords: moving-horizon estimation
bayesian-estimation
data reconciliation
approximations
systems
Issue Date: 2010
Publisher: AMER CHEMICAL SOC
Citation: INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 49(5), 2242-2253
Abstract: 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.
URI: http://dx.doi.org/10.1021/ie900197s
http://dspace.library.iitb.ac.in/xmlui/handle/10054/3767
http://hdl.handle.net/10054/3767
ISSN: 0888-5885
Appears in Collections:Article

Files in This Item:

There are no files associated with this item.

View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback