Record Details

Modified unscented recursive nonlinear dynamic data reconciliation for constrained state estimation

DSpace at IIT Bombay

View Archive Info
 
 
Field Value
 
Title Modified unscented recursive nonlinear dynamic data reconciliation for constrained state estimation
 
Creator KADU, SC
BHUSHAN, M
GUDI, R
ROY, K
 
Subject discrete-time-systems
eastman challenge process
extended kalman filter
observer
kalman filter
modified urnddr
constrained state estimation
 
Description In state estimation problems, often, the true states satisfy certain constraints resulting from the physics of the problem, that need to be incorporated and satisfied during the estimation procedure. Amongst various constrained nonlinear state estimation algorithms proposed in the literature, the unscented recursive nonlinear dynamic data reconciliation (URNDDR) presented in [1] seems to be promising since it is able to incorporate constraints while maintaining the recursive nature of estimation. In this article, we propose a modified URNDDR algorithm that gives superior performance when compared with the basic URNDDR. The improvements are obtained via better constraint handling and are demonstrated on representative case studies [2,3]. In addition to this modification, an efficient strategy combining basic unscented Kalman filter (UKF), URNDDR and modified URNDDR is also proposed in this article for solving large scale state estimation problems at relatively low computational cost. The utility of the proposed strategy is demonstrated by applying it to the Tennessee Eastman challenge process [4].
 
Publisher ELSEVIER SCI LTD
 
Date 2011-07-22T08:22:00Z
2011-12-26T12:52:21Z
2011-12-27T05:35:26Z
2011-07-22T08:22:00Z
2011-12-26T12:52:21Z
2011-12-27T05:35:26Z
2010
 
Type Article
 
Identifier JOURNAL OF PROCESS CONTROL, 20(4), 525-537
0959-1524
http://dx.doi.org/10.1016/j.jprocont.2010.02.006
http://dspace.library.iitb.ac.in/xmlui/handle/10054/6164
http://hdl.handle.net/10054/6164
 
Language en