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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Published in | PloS one Vol. 13; no. 12; p. e0208071 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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Public Library of Science
04.12.2018
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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. |
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AbstractList | 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. 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.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. |
Audience | Academic |
Author | Lee, Junho Chang, Hyuk-Jun |
AuthorAffiliation | Tsinghua University, CHINA 1 Department of Secured Smart Electric Vehicle, Kookmin University, Seoul, Republic of Korea 2 School of Electrical Engineering and Department of Secured Smart Electric Vehicle, Kookmin University, Seoul, Republic of Korea |
AuthorAffiliation_xml | – name: 2 School of Electrical Engineering and Department of Secured Smart Electric Vehicle, Kookmin University, Seoul, Republic of Korea – name: 1 Department of Secured Smart Electric Vehicle, Kookmin University, Seoul, Republic of Korea – name: Tsinghua University, CHINA |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30513086$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1016_j_compchemeng_2020_106801 crossref_primary_10_1007_s10462_022_10137_0 crossref_primary_10_1080_15397734_2023_2227703 crossref_primary_10_1016_j_apenergy_2024_125197 crossref_primary_10_1109_ACCESS_2020_3031037 crossref_primary_10_3390_wevj13120224 crossref_primary_10_1007_s11081_020_09535_6 crossref_primary_10_1080_23249935_2024_2368010 crossref_primary_10_1016_j_isatra_2020_08_023 |
Cites_doi | 10.1007/s12555-010-0512-1 10.1016/j.automatica.2018.04.040 10.1023/A:1019241000636 10.1109/TAC.2003.811259 10.1007/s12239-012-0008-5 10.1007/s12239-014-0048-0 10.1016/S0005-1098(01)00174-1 10.1109/ITEC-AP.2014.6941219 10.4271/2006-01-0873 10.1016/j.compchemeng.2012.06.031 10.1016/j.sysarc.2016.04.008 10.1016/j.apenergy.2017.11.072 10.1016/j.enconman.2015.12.077 10.1016/j.jprocont.2014.07.005 10.1016/j.energy.2018.01.152 10.1016/0005-1098(94)90159-7 10.1002/aic.11965 10.1109/ACC.2008.4586495 10.1109/TIE.2008.2008793 10.1109/TIE.2009.2015748 10.1109/ICMSAO.2013.6552547 10.1109/TCST.2012.2198478 10.1109/TVT.2018.2806400 10.1109/TITS.2003.811644 10.1007/978-1-4614-1433-9_2 10.1109/TPEL.2012.2219593 10.1109/TCST.2011.2124461 10.1016/j.conengprac.2013.10.005 10.1109/TIE.2016.2583400 |
ContentType | Journal Article |
Copyright | COPYRIGHT 2018 Public Library of Science 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. 2018 Lee, Chang 2018 Lee, Chang |
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SubjectTerms | 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 |
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Title | Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver |
URI | https://www.ncbi.nlm.nih.gov/pubmed/30513086 https://www.proquest.com/docview/2149884565 https://www.proquest.com/docview/2150527113 https://pubmed.ncbi.nlm.nih.gov/PMC6279033 https://doaj.org/article/8c43bcf1515a40b5b8bd5dc215e78618 http://dx.doi.org/10.1371/journal.pone.0208071 |
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