ERA

Download the full-sized PDF of Filtering Approaches for Inequality Constrained Parameter EstimationDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3SH86

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

Filtering Approaches for Inequality Constrained Parameter Estimation Open Access

Descriptions

Other title
Subject/Keyword
Moving horizon estimation
Ensemble Kalman filter
Unscented Kalman filter
Inequality constraints
Parameter estimation
Constrained estimation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Yang, Xiongtan
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Prasad, Vinay (Chemical and Materials Engineering)
Examining committee member and department
Trivedi, Japan (Mining and Petroleum Engineering)
Huang, Biao (Chemical and Materials Engineering)
Prasad, Vinay (Chemical and Materials Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Chemical Engineering
Date accepted
2013-01-21T15:50:53Z
Graduation date
2013-06
Degree
Master of Science
Degree level
Master's
Abstract
Parameter estimation of a dynamic system is an important task in process systems engineering. The utilization of an augmented system offers the approach of estimating process states and parameters simultaneously. In practice, the parameters often satisfy certain constraints which should be incorporated to improve the estimation performance. This thesis focuses on the inequality constrained parameter estimation problem. We introduce a method of constructing inequality constraints on parameters from routine steady-state operation data. A constraint implementation method with the unscented Kalman filter (UKF) is proposed that yields faster recovery of parameter estimates than the conventional projection method. The appropriate use of projection method with the ensemble Kalman filter (EnKF) is introduced. Also, a constrained estimation method with the EnKF is proposed which results in improved performance compared to the projection method. For the moving horizon estimation (MHE), we propose an alternative approach for constrained parameter estimation, which provides better performance than the directly constrained MHE. The efficacies of the proposed approaches in this thesis are evaluated using several simulated process examples.
Language
English
DOI
doi:10.7939/R3SH86
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication

File Details

Date Uploaded
Date Modified
2014-04-29T18:18:04.613+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 1971807
Last modified: 2015:10:12 16:55:37-06:00
Filename: Yang_Xiongtan_Spring 2013.pdf
Original checksum: 1ec5503248a3051964bb34c962eee847
Well formed: false
Valid: false
Status message: Unexpected error in findFonts java.lang.ClassCastException: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject cannot be cast to edu.harvard.hul.ois.jhove.module.pdf.PdfDictionary offset=3540
Page count: 93
Activity of users you follow
User Activity Date