Loading…
Model Predictive Control for High-speed Train with Automatic Trajectory Configuration and Tractive Force Optimization
High-speed train transportation is organized in a way of globally centralized planning and locally autonomous adjustment with the real-time known positions, speeds and other state information of trains. The hierarchical integration architecture composed of top, middle and bottom levels is proposed b...
Saved in:
Published in: | Computer modeling in engineering & sciences 2013, Vol.90 (6), p.415-437 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 437 |
container_issue | 6 |
container_start_page | 415 |
container_title | Computer modeling in engineering & sciences |
container_volume | 90 |
creator | Zhou, Yonghua Yang, Xun Chao, Mi |
description | High-speed train transportation is organized in a way of globally centralized planning and locally autonomous adjustment with the real-time known positions, speeds and other state information of trains. The hierarchical integration architecture composed of top, middle and bottom levels is proposed based on model predictive control (MPC) for the real-time scheduling and control. The middle-level trajectory configuration and tractive force setpoints play a critical role in fulfilling the top-level scheduling commands and guaranteeing the controllability of bottomlevel train operations. In the middle-level MPC-based train operation planning, the continuous cellular automaton model of train movements is proposed to dynamically configure the train operation positions and speeds at appointed time, which synthetically considers the scheduling strategies from the top layer, and the tempospatial constraints and operation statuses at the bottom level. The macroscopic dynamic model of a train predicts the trajectories under the candidate control sequences. Through Levenberg-Marquardt optimization, the feasible tractive forces and updated trajectories are attained under the power constraints of electric machines. Numerical results have demonstrated the effectiveness of proposed control planning technique. This paper reveals the utilities of different-level models of train movements for the accomplishment of railway network operation optimization and the guaranty of individual train operation safety. It also provides a solution to automatic trajectory configuration in the automatic train protection (ATP) and operation (ATO) systems. |
doi_str_mv | 10.3970/cmes.2013.090.415 |
format | article |
fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_miscellaneous_1762095144</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1762095144</sourcerecordid><originalsourceid>FETCH-LOGICAL-p216t-a6b043f3d5f4219e3844fc23932e47862382db428564e61be79d20f8a9e276983</originalsourceid><addsrcrecordid>eNpdTrtOwzAUtRBIlMIHsFliYUnwO_FYVZQiFZWhzFWaXLeukjjYDgi-nrSFhelcnXteCN1SknKdkYeygZAyQnlKNEkFlWdoRCVTCZVEnf_dQrNLdBXCnhCueK5HqH9xFdT41UNly2g_AE9dG72rsXEez-12l4QOoMIrX9gWf9q4w5M-uqaItjyQeyij818Hm7Hb3g-8a3HRHh2nxJnzJeBlF21jv4__a3RhijrAzS-O0dvscTWdJ4vl0_N0skg6RlVMCrUhghteSSMY1cBzIUzJuOYMRJYrxnNWbQTLpRKg6AYyXTFi8kIDy5TO-Rjdn3I77957CHHd2FBCXRctuD6saaYY0ZIKMUjv_kn3rvftsG49FGacaqko_wGFfmzD</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2397319561</pqid></control><display><type>article</type><title>Model Predictive Control for High-speed Train with Automatic Trajectory Configuration and Tractive Force Optimization</title><source>Publicly Available Content Database</source><creator>Zhou, Yonghua ; Yang, Xun ; Chao, Mi</creator><creatorcontrib>Zhou, Yonghua ; Yang, Xun ; Chao, Mi</creatorcontrib><description>High-speed train transportation is organized in a way of globally centralized planning and locally autonomous adjustment with the real-time known positions, speeds and other state information of trains. The hierarchical integration architecture composed of top, middle and bottom levels is proposed based on model predictive control (MPC) for the real-time scheduling and control. The middle-level trajectory configuration and tractive force setpoints play a critical role in fulfilling the top-level scheduling commands and guaranteeing the controllability of bottomlevel train operations. In the middle-level MPC-based train operation planning, the continuous cellular automaton model of train movements is proposed to dynamically configure the train operation positions and speeds at appointed time, which synthetically considers the scheduling strategies from the top layer, and the tempospatial constraints and operation statuses at the bottom level. The macroscopic dynamic model of a train predicts the trajectories under the candidate control sequences. Through Levenberg-Marquardt optimization, the feasible tractive forces and updated trajectories are attained under the power constraints of electric machines. Numerical results have demonstrated the effectiveness of proposed control planning technique. This paper reveals the utilities of different-level models of train movements for the accomplishment of railway network operation optimization and the guaranty of individual train operation safety. It also provides a solution to automatic trajectory configuration in the automatic train protection (ATP) and operation (ATO) systems.</description><identifier>ISSN: 1526-1492</identifier><identifier>EISSN: 1526-1506</identifier><identifier>DOI: 10.3970/cmes.2013.090.415</identifier><language>eng</language><publisher>Henderson: Tech Science Press</publisher><subject>Automatic control ; Automation ; Cellular automata ; Configurations ; Controllability ; Dynamic models ; High speed rail ; High speed trains ; Mathematical models ; Optimization ; Predictive control ; Real time ; Scheduling ; Stability ; Traction force ; Trains ; Trajectories ; Trajectory control ; Utilities</subject><ispartof>Computer modeling in engineering & sciences, 2013, Vol.90 (6), p.415-437</ispartof><rights>2013. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2397319561?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,4024,25753,27923,27924,27925,37012,37013,44590</link.rule.ids></links><search><creatorcontrib>Zhou, Yonghua</creatorcontrib><creatorcontrib>Yang, Xun</creatorcontrib><creatorcontrib>Chao, Mi</creatorcontrib><title>Model Predictive Control for High-speed Train with Automatic Trajectory Configuration and Tractive Force Optimization</title><title>Computer modeling in engineering & sciences</title><description>High-speed train transportation is organized in a way of globally centralized planning and locally autonomous adjustment with the real-time known positions, speeds and other state information of trains. The hierarchical integration architecture composed of top, middle and bottom levels is proposed based on model predictive control (MPC) for the real-time scheduling and control. The middle-level trajectory configuration and tractive force setpoints play a critical role in fulfilling the top-level scheduling commands and guaranteeing the controllability of bottomlevel train operations. In the middle-level MPC-based train operation planning, the continuous cellular automaton model of train movements is proposed to dynamically configure the train operation positions and speeds at appointed time, which synthetically considers the scheduling strategies from the top layer, and the tempospatial constraints and operation statuses at the bottom level. The macroscopic dynamic model of a train predicts the trajectories under the candidate control sequences. Through Levenberg-Marquardt optimization, the feasible tractive forces and updated trajectories are attained under the power constraints of electric machines. Numerical results have demonstrated the effectiveness of proposed control planning technique. This paper reveals the utilities of different-level models of train movements for the accomplishment of railway network operation optimization and the guaranty of individual train operation safety. It also provides a solution to automatic trajectory configuration in the automatic train protection (ATP) and operation (ATO) systems.</description><subject>Automatic control</subject><subject>Automation</subject><subject>Cellular automata</subject><subject>Configurations</subject><subject>Controllability</subject><subject>Dynamic models</subject><subject>High speed rail</subject><subject>High speed trains</subject><subject>Mathematical models</subject><subject>Optimization</subject><subject>Predictive control</subject><subject>Real time</subject><subject>Scheduling</subject><subject>Stability</subject><subject>Traction force</subject><subject>Trains</subject><subject>Trajectories</subject><subject>Trajectory control</subject><subject>Utilities</subject><issn>1526-1492</issn><issn>1526-1506</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpdTrtOwzAUtRBIlMIHsFliYUnwO_FYVZQiFZWhzFWaXLeukjjYDgi-nrSFhelcnXteCN1SknKdkYeygZAyQnlKNEkFlWdoRCVTCZVEnf_dQrNLdBXCnhCueK5HqH9xFdT41UNly2g_AE9dG72rsXEez-12l4QOoMIrX9gWf9q4w5M-uqaItjyQeyij818Hm7Hb3g-8a3HRHh2nxJnzJeBlF21jv4__a3RhijrAzS-O0dvscTWdJ4vl0_N0skg6RlVMCrUhghteSSMY1cBzIUzJuOYMRJYrxnNWbQTLpRKg6AYyXTFi8kIDy5TO-Rjdn3I77957CHHd2FBCXRctuD6saaYY0ZIKMUjv_kn3rvftsG49FGacaqko_wGFfmzD</recordid><startdate>2013</startdate><enddate>2013</enddate><creator>Zhou, Yonghua</creator><creator>Yang, Xun</creator><creator>Chao, Mi</creator><general>Tech Science Press</general><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>2013</creationdate><title>Model Predictive Control for High-speed Train with Automatic Trajectory Configuration and Tractive Force Optimization</title><author>Zhou, Yonghua ; Yang, Xun ; Chao, Mi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p216t-a6b043f3d5f4219e3844fc23932e47862382db428564e61be79d20f8a9e276983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Automatic control</topic><topic>Automation</topic><topic>Cellular automata</topic><topic>Configurations</topic><topic>Controllability</topic><topic>Dynamic models</topic><topic>High speed rail</topic><topic>High speed trains</topic><topic>Mathematical models</topic><topic>Optimization</topic><topic>Predictive control</topic><topic>Real time</topic><topic>Scheduling</topic><topic>Stability</topic><topic>Traction force</topic><topic>Trains</topic><topic>Trajectories</topic><topic>Trajectory control</topic><topic>Utilities</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Yonghua</creatorcontrib><creatorcontrib>Yang, Xun</creatorcontrib><creatorcontrib>Chao, Mi</creatorcontrib><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>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><jtitle>Computer modeling in engineering & sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Yonghua</au><au>Yang, Xun</au><au>Chao, Mi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Model Predictive Control for High-speed Train with Automatic Trajectory Configuration and Tractive Force Optimization</atitle><jtitle>Computer modeling in engineering & sciences</jtitle><date>2013</date><risdate>2013</risdate><volume>90</volume><issue>6</issue><spage>415</spage><epage>437</epage><pages>415-437</pages><issn>1526-1492</issn><eissn>1526-1506</eissn><abstract>High-speed train transportation is organized in a way of globally centralized planning and locally autonomous adjustment with the real-time known positions, speeds and other state information of trains. The hierarchical integration architecture composed of top, middle and bottom levels is proposed based on model predictive control (MPC) for the real-time scheduling and control. The middle-level trajectory configuration and tractive force setpoints play a critical role in fulfilling the top-level scheduling commands and guaranteeing the controllability of bottomlevel train operations. In the middle-level MPC-based train operation planning, the continuous cellular automaton model of train movements is proposed to dynamically configure the train operation positions and speeds at appointed time, which synthetically considers the scheduling strategies from the top layer, and the tempospatial constraints and operation statuses at the bottom level. The macroscopic dynamic model of a train predicts the trajectories under the candidate control sequences. Through Levenberg-Marquardt optimization, the feasible tractive forces and updated trajectories are attained under the power constraints of electric machines. Numerical results have demonstrated the effectiveness of proposed control planning technique. This paper reveals the utilities of different-level models of train movements for the accomplishment of railway network operation optimization and the guaranty of individual train operation safety. It also provides a solution to automatic trajectory configuration in the automatic train protection (ATP) and operation (ATO) systems.</abstract><cop>Henderson</cop><pub>Tech Science Press</pub><doi>10.3970/cmes.2013.090.415</doi><tpages>23</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1526-1492 |
ispartof | Computer modeling in engineering & sciences, 2013, Vol.90 (6), p.415-437 |
issn | 1526-1492 1526-1506 |
language | eng |
recordid | cdi_proquest_miscellaneous_1762095144 |
source | Publicly Available Content Database |
subjects | Automatic control Automation Cellular automata Configurations Controllability Dynamic models High speed rail High speed trains Mathematical models Optimization Predictive control Real time Scheduling Stability Traction force Trains Trajectories Trajectory control Utilities |
title | Model Predictive Control for High-speed Train with Automatic Trajectory Configuration and Tractive Force Optimization |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T07%3A55%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Model%20Predictive%20Control%20for%20High-speed%20Train%20with%20Automatic%20Trajectory%20Configuration%20and%20Tractive%20Force%20Optimization&rft.jtitle=Computer%20modeling%20in%20engineering%20&%20sciences&rft.au=Zhou,%20Yonghua&rft.date=2013&rft.volume=90&rft.issue=6&rft.spage=415&rft.epage=437&rft.pages=415-437&rft.issn=1526-1492&rft.eissn=1526-1506&rft_id=info:doi/10.3970/cmes.2013.090.415&rft_dat=%3Cproquest%3E1762095144%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p216t-a6b043f3d5f4219e3844fc23932e47862382db428564e61be79d20f8a9e276983%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2397319561&rft_id=info:pmid/&rfr_iscdi=true |