Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver
Explicit model predictive control (eMPC) has been proposed to reduce the huge computational complexity of MPC while maintaining the performance of MPC. Therefore, this control method has been more widely employed in the automotive industry than MPC. In this paper, an eMPC is designed to perform a do...
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| creator | Lee, Junho Chang, Hyuk-Jun |
| description | Explicit model predictive control (eMPC) has been proposed to reduce the huge computational complexity of MPC while maintaining the performance of MPC. Therefore, this control method has been more widely employed in the automotive industry than MPC. In this paper, an eMPC is designed to perform a double-lane-change (DLC) maneuver. This task has been employed to demonstrate the efficacy of controllers in an autonomous driving situation. In this sense, the proposed controller shows better performance than a driver model designed in CarSim at a high vehicle longitudinal velocity. The main contribution of this paper is to present an eMPC for discrete-time linear time-variant (LTV) systems so that the proposed controller can be robust against parameter variation. In a state-space representation of the vehicle, the longitudinal velocity of the vehicle is assumed to be a constant so that the whole system is linear time-invariant (LTI). However, it is inevitable that this velocity varies in an actual driving situation. Therefore, an eMPC controller is designed using an add-on unit to consider the varying parameter without modification of the eMPC solution. The CarSim simulation results of eMPC show enhanced performance compared to that of eMPC for the LTI system. |
| doi_str_mv | 10.1371/journal.pone.0208071 |
| format | Article |
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Therefore, this control method has been more widely employed in the automotive industry than MPC. In this paper, an eMPC is designed to perform a double-lane-change (DLC) maneuver. This task has been employed to demonstrate the efficacy of controllers in an autonomous driving situation. In this sense, the proposed controller shows better performance than a driver model designed in CarSim at a high vehicle longitudinal velocity. The main contribution of this paper is to present an eMPC for discrete-time linear time-variant (LTV) systems so that the proposed controller can be robust against parameter variation. In a state-space representation of the vehicle, the longitudinal velocity of the vehicle is assumed to be a constant so that the whole system is linear time-invariant (LTI). However, it is inevitable that this velocity varies in an actual driving situation. Therefore, an eMPC controller is designed using an add-on unit to consider the varying parameter without modification of the eMPC solution. The CarSim simulation results of eMPC show enhanced performance compared to that of eMPC for the LTI system.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0208071</identifier><identifier>PMID: 30513086</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Automation - methods ; Automobile Driving ; Automobile industry ; Automobiles ; Automotive industry ; Computer applications ; Computer Simulation ; Control algorithms ; Control systems design ; Control theory ; Controllers ; Discrete time systems ; Electric vehicles ; Embedded systems ; Energy efficiency ; Engineering and Technology ; Equipment Design ; Lane changing ; Linear Models ; Linear programming ; Mathematical models ; Parameter modification ; Parameter robustness ; Performance enhancement ; Physical Sciences ; Planning ; Predictive control ; Process controls ; Research and Analysis Methods ; Robust control ; Simulation ; Social Sciences ; Software ; State space models ; Technology application ; Time Factors ; Time varying control systems ; Velocity</subject><ispartof>PloS one, 2018-12, Vol.13 (12), p.e0208071-e0208071</ispartof><rights>COPYRIGHT 2018 Public Library of Science</rights><rights>2018 Lee, Chang. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 Lee, Chang 2018 Lee, Chang</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-68443c50cc28ac980e4b6d9a56e86ca8b62586d1f19bb808fe2fb2e144b7c2703</citedby><cites>FETCH-LOGICAL-c692t-68443c50cc28ac980e4b6d9a56e86ca8b62586d1f19bb808fe2fb2e144b7c2703</cites><orcidid>0000-0001-7781-2961 ; 0000-0001-8969-1714</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279033/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279033/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30513086$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Junho</creatorcontrib><creatorcontrib>Chang, Hyuk-Jun</creatorcontrib><title>Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Explicit model predictive control (eMPC) has been proposed to reduce the huge computational complexity of MPC while maintaining the performance of MPC. Therefore, this control method has been more widely employed in the automotive industry than MPC. In this paper, an eMPC is designed to perform a double-lane-change (DLC) maneuver. This task has been employed to demonstrate the efficacy of controllers in an autonomous driving situation. In this sense, the proposed controller shows better performance than a driver model designed in CarSim at a high vehicle longitudinal velocity. The main contribution of this paper is to present an eMPC for discrete-time linear time-variant (LTV) systems so that the proposed controller can be robust against parameter variation. In a state-space representation of the vehicle, the longitudinal velocity of the vehicle is assumed to be a constant so that the whole system is linear time-invariant (LTI). However, it is inevitable that this velocity varies in an actual driving situation. Therefore, an eMPC controller is designed using an add-on unit to consider the varying parameter without modification of the eMPC solution. The CarSim simulation results of eMPC show enhanced performance compared to that of eMPC for the LTI system.</description><subject>Algorithms</subject><subject>Automation - methods</subject><subject>Automobile Driving</subject><subject>Automobile industry</subject><subject>Automobiles</subject><subject>Automotive industry</subject><subject>Computer applications</subject><subject>Computer Simulation</subject><subject>Control algorithms</subject><subject>Control systems design</subject><subject>Control theory</subject><subject>Controllers</subject><subject>Discrete time systems</subject><subject>Electric vehicles</subject><subject>Embedded systems</subject><subject>Energy efficiency</subject><subject>Engineering and Technology</subject><subject>Equipment Design</subject><subject>Lane changing</subject><subject>Linear Models</subject><subject>Linear programming</subject><subject>Mathematical models</subject><subject>Parameter modification</subject><subject>Parameter robustness</subject><subject>Performance enhancement</subject><subject>Physical Sciences</subject><subject>Planning</subject><subject>Predictive control</subject><subject>Process controls</subject><subject>Research and Analysis Methods</subject><subject>Robust control</subject><subject>Simulation</subject><subject>Social Sciences</subject><subject>Software</subject><subject>State space models</subject><subject>Technology application</subject><subject>Time Factors</subject><subject>Time varying control systems</subject><subject>Velocity</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk01r3DAQhk1padK0_6C0hkJpD97qw5LlSyCEtF0IBPp1FbI83lWQLUeSt8m_r911wrrkUHSQkJ55R3o1kySvMVphWuBP127wnbKr3nWwQgQJVOAnyTEuKck4QfTpwfooeRHCNUKMCs6fJ0cUMUyR4MfJzcVtb402MW1dDTbtPdRGR7ODVLsuemfTxvnUmg6UT6NpIdspb1QX03AXIrQh_W3iNlX9JKOicV0aXVq7obKQWdVBpreq20DajuthB_5l8qxRNsCreT5Jfn6--HH-Nbu8-rI-P7vMNC9JzLjIc6oZ0poIpUuBIK94XSrGQXCtRMUJE7zGDS6rSiDRAGkqAjjPq0KTAtGT5O1et7cuyNmtIAnOSyFyxtlIrPdE7dS17L1plb-TThn5d8P5jVQ-Gm1BCp3TSjeYYaZyVLFKVDWrNcEMCsGxGLVO52xD1UKtYfRO2YXo8qQzW7lxO8lJUSJKR4EPs4B3NwOEKFsTNNjJQjdM92aIkQLjCX33D_r462Zqo8YHmK5xY149icozxnOKOMX5SK0eocZRQ2vGCoDGjPuLgI-LgKlK4DZu1BCCXH__9v_s1a8l-_6A3YKycRucHaaKCksw34PauxA8NA8mYySnzrh3Q06dIefOGMPeHH7QQ9B9K9A_rDIKBw</recordid><startdate>20181204</startdate><enddate>20181204</enddate><creator>Lee, Junho</creator><creator>Chang, Hyuk-Jun</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7781-2961</orcidid><orcidid>https://orcid.org/0000-0001-8969-1714</orcidid></search><sort><creationdate>20181204</creationdate><title>Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver</title><author>Lee, Junho ; Chang, Hyuk-Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-68443c50cc28ac980e4b6d9a56e86ca8b62586d1f19bb808fe2fb2e144b7c2703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Automation - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Junho</au><au>Chang, Hyuk-Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2018-12-04</date><risdate>2018</risdate><volume>13</volume><issue>12</issue><spage>e0208071</spage><epage>e0208071</epage><pages>e0208071-e0208071</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Explicit model predictive control (eMPC) has been proposed to reduce the huge computational complexity of MPC while maintaining the performance of MPC. Therefore, this control method has been more widely employed in the automotive industry than MPC. In this paper, an eMPC is designed to perform a double-lane-change (DLC) maneuver. This task has been employed to demonstrate the efficacy of controllers in an autonomous driving situation. In this sense, the proposed controller shows better performance than a driver model designed in CarSim at a high vehicle longitudinal velocity. The main contribution of this paper is to present an eMPC for discrete-time linear time-variant (LTV) systems so that the proposed controller can be robust against parameter variation. In a state-space representation of the vehicle, the longitudinal velocity of the vehicle is assumed to be a constant so that the whole system is linear time-invariant (LTI). However, it is inevitable that this velocity varies in an actual driving situation. Therefore, an eMPC controller is designed using an add-on unit to consider the varying parameter without modification of the eMPC solution. The CarSim simulation results of eMPC show enhanced performance compared to that of eMPC for the LTI system.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30513086</pmid><doi>10.1371/journal.pone.0208071</doi><tpages>e0208071</tpages><orcidid>https://orcid.org/0000-0001-7781-2961</orcidid><orcidid>https://orcid.org/0000-0001-8969-1714</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Automation - methods Automobile Driving Automobile industry Automobiles Automotive industry Computer applications Computer Simulation Control algorithms Control systems design Control theory Controllers Discrete time systems Electric vehicles Embedded systems Energy efficiency Engineering and Technology Equipment Design Lane changing Linear Models Linear programming Mathematical models Parameter modification Parameter robustness Performance enhancement Physical Sciences Planning Predictive control Process controls Research and Analysis Methods Robust control Simulation Social Sciences Software State space models Technology application Time Factors Time varying control systems Velocity |
| title | Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver |
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