Constrained state estimation using the ensemble Kalman filter

被引：12

作者：

Prakash, J. ^{[1
]}

Patwardhan, Sachin C. ^{[3
]}

Shah, Sirish L. ^{[2
]}

机构：

[1] Anna Univ, Dept Instrumentat Engn, MIT Campus, Chennai 600025, Tamil Nadu, India

[2] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2M7, Canada

[3] Indian Inst Technol, Dept Chem Engn Q, Bombay 110016, Maharashtra, India

来源：

2008 AMERICAN CONTROL CONFERENCE, VOLS 1-12 | 2008年

基金：

加拿大自然科学与工程研究理事会;

关键词：

D O I：

10.1109/ACC.2008.4587042

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：3542 / +

页数：2

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