Loading…

GMV and restricted-structure GMV controller performance assessment multivariable case

The application of control loop performance assessment and benchmarking techniques to multivariable industrial process control loops is considered. The results for assessing the performance of multiple-input, multiple-output (MIMO) control loops against the generalized minimum variance (GMV) benchma...

Full description

Saved in:
Bibliographic Details
Main Authors: Majecki, P., Grimble, M.J.
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 702 vol.1
container_issue
container_start_page 697
container_title
container_volume 1
creator Majecki, P.
Grimble, M.J.
description The application of control loop performance assessment and benchmarking techniques to multivariable industrial process control loops is considered. The results for assessing the performance of multiple-input, multiple-output (MIMO) control loops against the generalized minimum variance (GMV) benchmark, using routine operating data and the knowledge of the interactor matrix are presented. Then, assuming knowledge of the system model, the optimal controller is restricted to be of a low-order classical structure so that a more realistic benchmark is obtained. The technique may also be used to determine the best structure to use for a multivariable controller. This paper presents an extension of the existing results to the cases of multivariable data-driven GMV benchmarking and multivariable model-based RS-GMV benchmarking.
doi_str_mv 10.23919/ACC.2004.1383685
format conference_proceeding
fullrecord <record><control><sourceid>proquest_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_1383685</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1383685</ieee_id><sourcerecordid>29790159</sourcerecordid><originalsourceid>FETCH-LOGICAL-i234t-ca5eee940ec83dac193440da487b90430750ec1abefacaceefea4c9db4d78cc53</originalsourceid><addsrcrecordid>eNpFkE9LxDAQxYN_wHXdDyBeetFb16RJmuS4FF2FFS-u1zJNpxBJ2zVpBb-9kRWEgRn4PWbeG0KuGV0X3DBzv6mqdUGpWDOueanlCVkUXOlc6pKdkpVRmqZKjEt1RhZUCZ6zkpkLchnjB6XMmJIuyH778p7B0GYB4xScnbDN0zDbaQ6Y_UI7DlMYvceQHTB0Y-hhsJhBjBhjj8OU9bOf3BcEB43HzELEK3LegY-4-utLsn98eKue8t3r9rna7HJXcDHlFiQiGkHRat6CZYYLQVsQWjWGCk6VTIhBgx1YsIgdgrCmbUSrtLWSL8ndce8hjJ9zSlD3Llr0HgYc51gXRhnKpEnC2z8hRAu-CymDi_UhuB7Cd800l0ymi0tyc9S5ZOwfH1_MfwDaGW-1</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>29790159</pqid></control><display><type>conference_proceeding</type><title>GMV and restricted-structure GMV controller performance assessment multivariable case</title><source>IEEE Xplore All Conference Series</source><creator>Majecki, P. ; Grimble, M.J.</creator><creatorcontrib>Majecki, P. ; Grimble, M.J.</creatorcontrib><description>The application of control loop performance assessment and benchmarking techniques to multivariable industrial process control loops is considered. The results for assessing the performance of multiple-input, multiple-output (MIMO) control loops against the generalized minimum variance (GMV) benchmark, using routine operating data and the knowledge of the interactor matrix are presented. Then, assuming knowledge of the system model, the optimal controller is restricted to be of a low-order classical structure so that a more realistic benchmark is obtained. The technique may also be used to determine the best structure to use for a multivariable controller. This paper presents an extension of the existing results to the cases of multivariable data-driven GMV benchmarking and multivariable model-based RS-GMV benchmarking.</description><identifier>ISSN: 0743-1619</identifier><identifier>ISBN: 9780780383357</identifier><identifier>ISBN: 0780383354</identifier><identifier>EISSN: 2378-5861</identifier><identifier>DOI: 10.23919/ACC.2004.1383685</identifier><language>eng</language><publisher>Piscataway NJ: IEEE</publisher><subject>Applied sciences ; Computer aided software engineering ; Computer science; control theory; systems ; Control theory. Systems ; Cost function ; Covariance matrix ; Electrical equipment industry ; Exact sciences and technology ; Industrial control ; MIMO ; Optimal control ; Performance analysis ; Process control ; Process control. Computer integrated manufacturing ; Stochastic systems</subject><ispartof>2004 American Control Conference Proceedings; Volume 1 of 6, 2004, Vol.1, p.697-702 vol.1</ispartof><rights>2007 INIST-CNRS</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1383685$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,2051,27903,27904,54533,54898,54910</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1383685$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=18351504$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Majecki, P.</creatorcontrib><creatorcontrib>Grimble, M.J.</creatorcontrib><title>GMV and restricted-structure GMV controller performance assessment multivariable case</title><title>2004 American Control Conference Proceedings; Volume 1 of 6</title><addtitle>ACC</addtitle><description>The application of control loop performance assessment and benchmarking techniques to multivariable industrial process control loops is considered. The results for assessing the performance of multiple-input, multiple-output (MIMO) control loops against the generalized minimum variance (GMV) benchmark, using routine operating data and the knowledge of the interactor matrix are presented. Then, assuming knowledge of the system model, the optimal controller is restricted to be of a low-order classical structure so that a more realistic benchmark is obtained. The technique may also be used to determine the best structure to use for a multivariable controller. This paper presents an extension of the existing results to the cases of multivariable data-driven GMV benchmarking and multivariable model-based RS-GMV benchmarking.</description><subject>Applied sciences</subject><subject>Computer aided software engineering</subject><subject>Computer science; control theory; systems</subject><subject>Control theory. Systems</subject><subject>Cost function</subject><subject>Covariance matrix</subject><subject>Electrical equipment industry</subject><subject>Exact sciences and technology</subject><subject>Industrial control</subject><subject>MIMO</subject><subject>Optimal control</subject><subject>Performance analysis</subject><subject>Process control</subject><subject>Process control. Computer integrated manufacturing</subject><subject>Stochastic systems</subject><issn>0743-1619</issn><issn>2378-5861</issn><isbn>9780780383357</isbn><isbn>0780383354</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFkE9LxDAQxYN_wHXdDyBeetFb16RJmuS4FF2FFS-u1zJNpxBJ2zVpBb-9kRWEgRn4PWbeG0KuGV0X3DBzv6mqdUGpWDOueanlCVkUXOlc6pKdkpVRmqZKjEt1RhZUCZ6zkpkLchnjB6XMmJIuyH778p7B0GYB4xScnbDN0zDbaQ6Y_UI7DlMYvceQHTB0Y-hhsJhBjBhjj8OU9bOf3BcEB43HzELEK3LegY-4-utLsn98eKue8t3r9rna7HJXcDHlFiQiGkHRat6CZYYLQVsQWjWGCk6VTIhBgx1YsIgdgrCmbUSrtLWSL8ndce8hjJ9zSlD3Llr0HgYc51gXRhnKpEnC2z8hRAu-CymDi_UhuB7Cd800l0ymi0tyc9S5ZOwfH1_MfwDaGW-1</recordid><startdate>20040101</startdate><enddate>20040101</enddate><creator>Majecki, P.</creator><creator>Grimble, M.J.</creator><general>IEEE</general><general>American Automatic Control Council</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>IQODW</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20040101</creationdate><title>GMV and restricted-structure GMV controller performance assessment multivariable case</title><author>Majecki, P. ; Grimble, M.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i234t-ca5eee940ec83dac193440da487b90430750ec1abefacaceefea4c9db4d78cc53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Applied sciences</topic><topic>Computer aided software engineering</topic><topic>Computer science; control theory; systems</topic><topic>Control theory. Systems</topic><topic>Cost function</topic><topic>Covariance matrix</topic><topic>Electrical equipment industry</topic><topic>Exact sciences and technology</topic><topic>Industrial control</topic><topic>MIMO</topic><topic>Optimal control</topic><topic>Performance analysis</topic><topic>Process control</topic><topic>Process control. Computer integrated manufacturing</topic><topic>Stochastic systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Majecki, P.</creatorcontrib><creatorcontrib>Grimble, M.J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Pascal-Francis</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science 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></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Majecki, P.</au><au>Grimble, M.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>GMV and restricted-structure GMV controller performance assessment multivariable case</atitle><btitle>2004 American Control Conference Proceedings; Volume 1 of 6</btitle><stitle>ACC</stitle><date>2004-01-01</date><risdate>2004</risdate><volume>1</volume><spage>697</spage><epage>702 vol.1</epage><pages>697-702 vol.1</pages><issn>0743-1619</issn><eissn>2378-5861</eissn><isbn>9780780383357</isbn><isbn>0780383354</isbn><abstract>The application of control loop performance assessment and benchmarking techniques to multivariable industrial process control loops is considered. The results for assessing the performance of multiple-input, multiple-output (MIMO) control loops against the generalized minimum variance (GMV) benchmark, using routine operating data and the knowledge of the interactor matrix are presented. Then, assuming knowledge of the system model, the optimal controller is restricted to be of a low-order classical structure so that a more realistic benchmark is obtained. The technique may also be used to determine the best structure to use for a multivariable controller. This paper presents an extension of the existing results to the cases of multivariable data-driven GMV benchmarking and multivariable model-based RS-GMV benchmarking.</abstract><cop>Piscataway NJ</cop><cop>Evanston IL</cop><pub>IEEE</pub><doi>10.23919/ACC.2004.1383685</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 0743-1619
ispartof 2004 American Control Conference Proceedings; Volume 1 of 6, 2004, Vol.1, p.697-702 vol.1
issn 0743-1619
2378-5861
language eng
recordid cdi_ieee_primary_1383685
source IEEE Xplore All Conference Series
subjects Applied sciences
Computer aided software engineering
Computer science
control theory
systems
Control theory. Systems
Cost function
Covariance matrix
Electrical equipment industry
Exact sciences and technology
Industrial control
MIMO
Optimal control
Performance analysis
Process control
Process control. Computer integrated manufacturing
Stochastic systems
title GMV and restricted-structure GMV controller performance assessment multivariable case
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T20%3A32%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=GMV%20and%20restricted-structure%20GMV%20controller%20performance%20assessment%20multivariable%20case&rft.btitle=2004%20American%20Control%20Conference%20Proceedings;%20Volume%201%20of%206&rft.au=Majecki,%20P.&rft.date=2004-01-01&rft.volume=1&rft.spage=697&rft.epage=702%20vol.1&rft.pages=697-702%20vol.1&rft.issn=0743-1619&rft.eissn=2378-5861&rft.isbn=9780780383357&rft.isbn_list=0780383354&rft_id=info:doi/10.23919/ACC.2004.1383685&rft_dat=%3Cproquest_CHZPO%3E29790159%3C/proquest_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i234t-ca5eee940ec83dac193440da487b90430750ec1abefacaceefea4c9db4d78cc53%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=29790159&rft_id=info:pmid/&rft_ieee_id=1383685&rfr_iscdi=true