Loading…
A comparison of implementation strategies for MPC
Four quadratic programming (QP) formulations of model predictive control (MPC) are compared with regards to ease of formulation, memory requirement, and numerical properties. The comparison is based on two example processes: a paper machine model, and a model of the Tennessee Eastman challenge proce...
Saved in:
Published in: | Modeling, identification and control identification and control, 2005, Vol.26 (1), p.39-50 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c397t-c3a1217f113c68b8762105b92bb22b44c7bab1aa5fda154f740d1db0eb29de393 |
---|---|
cites | cdi_FETCH-LOGICAL-c397t-c3a1217f113c68b8762105b92bb22b44c7bab1aa5fda154f740d1db0eb29de393 |
container_end_page | 50 |
container_issue | 1 |
container_start_page | 39 |
container_title | Modeling, identification and control |
container_volume | 26 |
creator | LIE, Bernt DIEZ, Marta Duenas HAUGE, Tor Anders |
description | Four quadratic programming (QP) formulations of model predictive control (MPC) are compared with regards to ease of formulation, memory requirement, and numerical properties. The comparison is based on two example processes: a paper machine model, and a model of the Tennessee Eastman challenge process; the number of free variables range from 150-1400. Five commercial QP solvers are compared. Preliminary results indicate that dense solvers still are the most efficient, but sparse solvers hold great promise. |
doi_str_mv | 10.4173/mic.2005.1.3 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_b97ce4657d27452a9ced888c978c3b2d</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_b97ce4657d27452a9ced888c978c3b2d</doaj_id><sourcerecordid>2709773801</sourcerecordid><originalsourceid>FETCH-LOGICAL-c397t-c3a1217f113c68b8762105b92bb22b44c7bab1aa5fda154f740d1db0eb29de393</originalsourceid><addsrcrecordid>eNpFkEtPwzAQhC0EEuVx4wdEQtxI8NpJbB-rikelIjjA2Vo7TuUqiYudHvj3pLSCy640mv12NITcAC1KEPyh97ZglFYFFPyEzEAqmgNn8pTMKOcsF7zi5-QipQ2lDKqSzgjMMxv6LUafwpCFNvP9tnO9G0Yc_aSkMeLo1t6lrA0xe31fXJGzFrvkro_7knw-PX4sXvLV2_NyMV_llisxThOBgWgBuK2lkaJmQCujmDGMmbK0wqABxKptcIrSipI20BjqDFON44pfkuWB2wTc6G30PcZvHdDrXyHEtcY4ets5bZSwrqwr0TBRVgyVdY2U0iohLTesmVi3B9Y2hq-dS6PehF0cpvgaKCslZ1TxyXV_cNkYUoqu_fsKVO8L1lPBel-wBr233x2hmCx2bcTB-vR_UwtVCwr8B5S4eVE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1024832093</pqid></control><display><type>article</type><title>A comparison of implementation strategies for MPC</title><source>Publicly Available Content Database</source><creator>LIE, Bernt ; DIEZ, Marta Duenas ; HAUGE, Tor Anders</creator><creatorcontrib>LIE, Bernt ; DIEZ, Marta Duenas ; HAUGE, Tor Anders</creatorcontrib><description>Four quadratic programming (QP) formulations of model predictive control (MPC) are compared with regards to ease of formulation, memory requirement, and numerical properties. The comparison is based on two example processes: a paper machine model, and a model of the Tennessee Eastman challenge process; the number of free variables range from 150-1400. Five commercial QP solvers are compared. Preliminary results indicate that dense solvers still are the most efficient, but sparse solvers hold great promise.</description><identifier>ISSN: 0332-7353</identifier><identifier>EISSN: 1890-1328</identifier><identifier>DOI: 10.4173/mic.2005.1.3</identifier><language>eng</language><publisher>Oslo: Research Council of Norway</publisher><subject>Applied sciences ; Computer science; control theory; systems ; Control theory. Systems ; Exact sciences and technology</subject><ispartof>Modeling, identification and control, 2005, Vol.26 (1), p.39-50</ispartof><rights>2005 INIST-CNRS</rights><rights>Copyright Norsk Forening for Automatisering (NFA) 2005</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c397t-c3a1217f113c68b8762105b92bb22b44c7bab1aa5fda154f740d1db0eb29de393</citedby><cites>FETCH-LOGICAL-c397t-c3a1217f113c68b8762105b92bb22b44c7bab1aa5fda154f740d1db0eb29de393</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1024832093?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,4024,25753,27923,27924,27925,37012,44590</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16796701$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>LIE, Bernt</creatorcontrib><creatorcontrib>DIEZ, Marta Duenas</creatorcontrib><creatorcontrib>HAUGE, Tor Anders</creatorcontrib><title>A comparison of implementation strategies for MPC</title><title>Modeling, identification and control</title><description>Four quadratic programming (QP) formulations of model predictive control (MPC) are compared with regards to ease of formulation, memory requirement, and numerical properties. The comparison is based on two example processes: a paper machine model, and a model of the Tennessee Eastman challenge process; the number of free variables range from 150-1400. Five commercial QP solvers are compared. Preliminary results indicate that dense solvers still are the most efficient, but sparse solvers hold great promise.</description><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Control theory. Systems</subject><subject>Exact sciences and technology</subject><issn>0332-7353</issn><issn>1890-1328</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpFkEtPwzAQhC0EEuVx4wdEQtxI8NpJbB-rikelIjjA2Vo7TuUqiYudHvj3pLSCy640mv12NITcAC1KEPyh97ZglFYFFPyEzEAqmgNn8pTMKOcsF7zi5-QipQ2lDKqSzgjMMxv6LUafwpCFNvP9tnO9G0Yc_aSkMeLo1t6lrA0xe31fXJGzFrvkro_7knw-PX4sXvLV2_NyMV_llisxThOBgWgBuK2lkaJmQCujmDGMmbK0wqABxKptcIrSipI20BjqDFON44pfkuWB2wTc6G30PcZvHdDrXyHEtcY4ets5bZSwrqwr0TBRVgyVdY2U0iohLTesmVi3B9Y2hq-dS6PehF0cpvgaKCslZ1TxyXV_cNkYUoqu_fsKVO8L1lPBel-wBr233x2hmCx2bcTB-vR_UwtVCwr8B5S4eVE</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>LIE, Bernt</creator><creator>DIEZ, Marta Duenas</creator><creator>HAUGE, Tor Anders</creator><general>Research Council of Norway</general><general>Norsk Forening for Automatisering (NFA)</general><general>Norwegian Society of Automatic Control</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7TB</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BFMQW</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>DOA</scope></search><sort><creationdate>2005</creationdate><title>A comparison of implementation strategies for MPC</title><author>LIE, Bernt ; DIEZ, Marta Duenas ; HAUGE, Tor Anders</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c397t-c3a1217f113c68b8762105b92bb22b44c7bab1aa5fda154f740d1db0eb29de393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Control theory. Systems</topic><topic>Exact sciences and technology</topic><toplevel>online_resources</toplevel><creatorcontrib>LIE, Bernt</creatorcontrib><creatorcontrib>DIEZ, Marta Duenas</creatorcontrib><creatorcontrib>HAUGE, Tor Anders</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Continental Europe Database</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Modeling, identification and control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>LIE, Bernt</au><au>DIEZ, Marta Duenas</au><au>HAUGE, Tor Anders</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comparison of implementation strategies for MPC</atitle><jtitle>Modeling, identification and control</jtitle><date>2005</date><risdate>2005</risdate><volume>26</volume><issue>1</issue><spage>39</spage><epage>50</epage><pages>39-50</pages><issn>0332-7353</issn><eissn>1890-1328</eissn><abstract>Four quadratic programming (QP) formulations of model predictive control (MPC) are compared with regards to ease of formulation, memory requirement, and numerical properties. The comparison is based on two example processes: a paper machine model, and a model of the Tennessee Eastman challenge process; the number of free variables range from 150-1400. Five commercial QP solvers are compared. Preliminary results indicate that dense solvers still are the most efficient, but sparse solvers hold great promise.</abstract><cop>Oslo</cop><pub>Research Council of Norway</pub><doi>10.4173/mic.2005.1.3</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0332-7353 |
ispartof | Modeling, identification and control, 2005, Vol.26 (1), p.39-50 |
issn | 0332-7353 1890-1328 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_b97ce4657d27452a9ced888c978c3b2d |
source | Publicly Available Content Database |
subjects | Applied sciences Computer science control theory systems Control theory. Systems Exact sciences and technology |
title | A comparison of implementation strategies for MPC |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T14%3A26%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20comparison%20of%20implementation%20strategies%20for%20MPC&rft.jtitle=Modeling,%20identification%20and%20control&rft.au=LIE,%20Bernt&rft.date=2005&rft.volume=26&rft.issue=1&rft.spage=39&rft.epage=50&rft.pages=39-50&rft.issn=0332-7353&rft.eissn=1890-1328&rft_id=info:doi/10.4173/mic.2005.1.3&rft_dat=%3Cproquest_doaj_%3E2709773801%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c397t-c3a1217f113c68b8762105b92bb22b44c7bab1aa5fda154f740d1db0eb29de393%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1024832093&rft_id=info:pmid/&rfr_iscdi=true |