Loading…

Distributed Double-Layered Dynamic Matrix Control for Large-Scale System

A distributed double-layered dynamic matrix control is proposed for large-scale systems. In the scheme, a large-scale system is first decomposed into several low-dimensional subsystems, and then a double-layered dynamic matrix control algorithm with distributed structure is developed for these subsy...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2022-05, Vol.2022, p.1-15
Main Authors: Wang, Li, Cai, Yuanli, Zan, Xin
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites cdi_FETCH-LOGICAL-c224t-641ee3b7c155b8d07dbaee84a754a6071e070af2e2247cb557366dbe0f0595493
container_end_page 15
container_issue
container_start_page 1
container_title Mathematical problems in engineering
container_volume 2022
creator Wang, Li
Cai, Yuanli
Zan, Xin
description A distributed double-layered dynamic matrix control is proposed for large-scale systems. In the scheme, a large-scale system is first decomposed into several low-dimensional subsystems, and then a double-layered dynamic matrix control algorithm with distributed structure is developed for these subsystems. The distributed open-loop prediction equation of each subsystem is formed based on the predicted output of each local subsystem and effects of its interconnecting neighbor subsystems. Due to simultaneous optimization, at each prediction, the coupling effects of neighbor subsystems are not available in time. Thus, the assumed value is utilized instead. In the economic optimization stage, conflicts may occur among different economic optimization goals. Utopia-tracking strategy is introduced to optimize multiple steady-state targets. Then, the obtained steady-state target values are taken as reference values and tracked in subsequent dynamic control. The actual control move for each subsystem is finally calculated. The proposed algorithm is tested on Shell heavy oil fractionator benchmark, and the effectiveness is demonstrated by comparing with the typical double-layered dynamic matrix control algorithm.
doi_str_mv 10.1155/2022/4650342
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2675437350</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2675437350</sourcerecordid><originalsourceid>FETCH-LOGICAL-c224t-641ee3b7c155b8d07dbaee84a754a6071e070af2e2247cb557366dbe0f0595493</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWKs3f8CCR43Nd9qjtNUKKx6q4C0ku7O6ZbupyS66_96U9uxpZuDhnZkHoWtK7imVcsIIYxOhJOGCnaARlYpjSYU-TT1hAlPGP87RRYwbQhiVdDpCq0Udu1C7voMyW_jeNYBzO0DYj0Nrt3WRvdhE_GZz33bBN1nlQ5bb8Al4XdgGsvUQO9heorPKNhGujnWM3h-Xb_MVzl-fnucPOS4YEx1WggJwp4t0r5uWRJfOAkyF1VJYRTQFoomtGCRaF05KzZUqHZCKyJkUMz5GN4fcXfDfPcTObHwf2rTSMJVCuObp_zG6O1BF8DEGqMwu1FsbBkOJ2bsye1fm6Crhtwf8q25L-1P_T_8BYaZnVw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2675437350</pqid></control><display><type>article</type><title>Distributed Double-Layered Dynamic Matrix Control for Large-Scale System</title><source>Publicly Available Content (ProQuest)</source><source>Wiley Open Access</source><creator>Wang, Li ; Cai, Yuanli ; Zan, Xin</creator><contributor>Muhammad, Taseer ; Taseer Muhammad</contributor><creatorcontrib>Wang, Li ; Cai, Yuanli ; Zan, Xin ; Muhammad, Taseer ; Taseer Muhammad</creatorcontrib><description>A distributed double-layered dynamic matrix control is proposed for large-scale systems. In the scheme, a large-scale system is first decomposed into several low-dimensional subsystems, and then a double-layered dynamic matrix control algorithm with distributed structure is developed for these subsystems. The distributed open-loop prediction equation of each subsystem is formed based on the predicted output of each local subsystem and effects of its interconnecting neighbor subsystems. Due to simultaneous optimization, at each prediction, the coupling effects of neighbor subsystems are not available in time. Thus, the assumed value is utilized instead. In the economic optimization stage, conflicts may occur among different economic optimization goals. Utopia-tracking strategy is introduced to optimize multiple steady-state targets. Then, the obtained steady-state target values are taken as reference values and tracked in subsequent dynamic control. The actual control move for each subsystem is finally calculated. The proposed algorithm is tested on Shell heavy oil fractionator benchmark, and the effectiveness is demonstrated by comparing with the typical double-layered dynamic matrix control algorithm.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2022/4650342</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Communication ; Control systems ; Control theory ; Decomposition ; Dynamic control ; Mathematical problems ; Optimization ; Predictions ; Process controls ; Steady state ; Subsystems</subject><ispartof>Mathematical problems in engineering, 2022-05, Vol.2022, p.1-15</ispartof><rights>Copyright © 2022 Li Wang et al.</rights><rights>Copyright © 2022 Li Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c224t-641ee3b7c155b8d07dbaee84a754a6071e070af2e2247cb557366dbe0f0595493</cites><orcidid>0000-0001-7364-3101 ; 0000-0002-1467-9347 ; 0000-0001-6398-4826</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2675437350/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2675437350?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><contributor>Muhammad, Taseer</contributor><contributor>Taseer Muhammad</contributor><creatorcontrib>Wang, Li</creatorcontrib><creatorcontrib>Cai, Yuanli</creatorcontrib><creatorcontrib>Zan, Xin</creatorcontrib><title>Distributed Double-Layered Dynamic Matrix Control for Large-Scale System</title><title>Mathematical problems in engineering</title><description>A distributed double-layered dynamic matrix control is proposed for large-scale systems. In the scheme, a large-scale system is first decomposed into several low-dimensional subsystems, and then a double-layered dynamic matrix control algorithm with distributed structure is developed for these subsystems. The distributed open-loop prediction equation of each subsystem is formed based on the predicted output of each local subsystem and effects of its interconnecting neighbor subsystems. Due to simultaneous optimization, at each prediction, the coupling effects of neighbor subsystems are not available in time. Thus, the assumed value is utilized instead. In the economic optimization stage, conflicts may occur among different economic optimization goals. Utopia-tracking strategy is introduced to optimize multiple steady-state targets. Then, the obtained steady-state target values are taken as reference values and tracked in subsequent dynamic control. The actual control move for each subsystem is finally calculated. The proposed algorithm is tested on Shell heavy oil fractionator benchmark, and the effectiveness is demonstrated by comparing with the typical double-layered dynamic matrix control algorithm.</description><subject>Algorithms</subject><subject>Communication</subject><subject>Control systems</subject><subject>Control theory</subject><subject>Decomposition</subject><subject>Dynamic control</subject><subject>Mathematical problems</subject><subject>Optimization</subject><subject>Predictions</subject><subject>Process controls</subject><subject>Steady state</subject><subject>Subsystems</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNp9kE1LAzEQhoMoWKs3f8CCR43Nd9qjtNUKKx6q4C0ku7O6ZbupyS66_96U9uxpZuDhnZkHoWtK7imVcsIIYxOhJOGCnaARlYpjSYU-TT1hAlPGP87RRYwbQhiVdDpCq0Udu1C7voMyW_jeNYBzO0DYj0Nrt3WRvdhE_GZz33bBN1nlQ5bb8Al4XdgGsvUQO9heorPKNhGujnWM3h-Xb_MVzl-fnucPOS4YEx1WggJwp4t0r5uWRJfOAkyF1VJYRTQFoomtGCRaF05KzZUqHZCKyJkUMz5GN4fcXfDfPcTObHwf2rTSMJVCuObp_zG6O1BF8DEGqMwu1FsbBkOJ2bsye1fm6Crhtwf8q25L-1P_T_8BYaZnVw</recordid><startdate>20220530</startdate><enddate>20220530</enddate><creator>Wang, Li</creator><creator>Cai, Yuanli</creator><creator>Zan, Xin</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</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>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><orcidid>https://orcid.org/0000-0001-7364-3101</orcidid><orcidid>https://orcid.org/0000-0002-1467-9347</orcidid><orcidid>https://orcid.org/0000-0001-6398-4826</orcidid></search><sort><creationdate>20220530</creationdate><title>Distributed Double-Layered Dynamic Matrix Control for Large-Scale System</title><author>Wang, Li ; Cai, Yuanli ; Zan, Xin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c224t-641ee3b7c155b8d07dbaee84a754a6071e070af2e2247cb557366dbe0f0595493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Communication</topic><topic>Control systems</topic><topic>Control theory</topic><topic>Decomposition</topic><topic>Dynamic control</topic><topic>Mathematical problems</topic><topic>Optimization</topic><topic>Predictions</topic><topic>Process controls</topic><topic>Steady state</topic><topic>Subsystems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Li</creatorcontrib><creatorcontrib>Cai, Yuanli</creatorcontrib><creatorcontrib>Zan, Xin</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East &amp; Africa Database</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content (ProQuest)</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><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Li</au><au>Cai, Yuanli</au><au>Zan, Xin</au><au>Muhammad, Taseer</au><au>Taseer Muhammad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distributed Double-Layered Dynamic Matrix Control for Large-Scale System</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2022-05-30</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>15</epage><pages>1-15</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>A distributed double-layered dynamic matrix control is proposed for large-scale systems. In the scheme, a large-scale system is first decomposed into several low-dimensional subsystems, and then a double-layered dynamic matrix control algorithm with distributed structure is developed for these subsystems. The distributed open-loop prediction equation of each subsystem is formed based on the predicted output of each local subsystem and effects of its interconnecting neighbor subsystems. Due to simultaneous optimization, at each prediction, the coupling effects of neighbor subsystems are not available in time. Thus, the assumed value is utilized instead. In the economic optimization stage, conflicts may occur among different economic optimization goals. Utopia-tracking strategy is introduced to optimize multiple steady-state targets. Then, the obtained steady-state target values are taken as reference values and tracked in subsequent dynamic control. The actual control move for each subsystem is finally calculated. The proposed algorithm is tested on Shell heavy oil fractionator benchmark, and the effectiveness is demonstrated by comparing with the typical double-layered dynamic matrix control algorithm.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/4650342</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-7364-3101</orcidid><orcidid>https://orcid.org/0000-0002-1467-9347</orcidid><orcidid>https://orcid.org/0000-0001-6398-4826</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1024-123X
ispartof Mathematical problems in engineering, 2022-05, Vol.2022, p.1-15
issn 1024-123X
1563-5147
language eng
recordid cdi_proquest_journals_2675437350
source Publicly Available Content (ProQuest); Wiley Open Access
subjects Algorithms
Communication
Control systems
Control theory
Decomposition
Dynamic control
Mathematical problems
Optimization
Predictions
Process controls
Steady state
Subsystems
title Distributed Double-Layered Dynamic Matrix Control for Large-Scale System
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T01%3A28%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Distributed%20Double-Layered%20Dynamic%20Matrix%20Control%20for%20Large-Scale%20System&rft.jtitle=Mathematical%20problems%20in%20engineering&rft.au=Wang,%20Li&rft.date=2022-05-30&rft.volume=2022&rft.spage=1&rft.epage=15&rft.pages=1-15&rft.issn=1024-123X&rft.eissn=1563-5147&rft_id=info:doi/10.1155/2022/4650342&rft_dat=%3Cproquest_cross%3E2675437350%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c224t-641ee3b7c155b8d07dbaee84a754a6071e070af2e2247cb557366dbe0f0595493%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2675437350&rft_id=info:pmid/&rfr_iscdi=true