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 inPloS one Vol. 13; no. 12; p. e0208071
Main Authors Lee, Junho, Chang, Hyuk-Jun
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 04.12.2018
Public Library of Science (PLoS)
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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.
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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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.
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Snippet Explicit model predictive control (eMPC) has been proposed to reduce the huge computational complexity of MPC while maintaining the performance of MPC....
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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
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https://doaj.org/article/8c43bcf1515a40b5b8bd5dc215e78618
http://dx.doi.org/10.1371/journal.pone.0208071
Volume 13
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