Model Predictive Control for Autonomous Driving Vehicles

The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle s...

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Autores principales:	Trieu Minh Vu, Reza Moezzi, Jindrich Cyrus, Jaroslav Hlava
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	trajectory tracking nonlinear model predictive control hard and softened constraints optimal control action tracking error Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/f7fc9c2706604e96b57a413f8bea1e2e
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id	oai:doaj.org-article:f7fc9c2706604e96b57a413f8bea1e2e
record_format	dspace
spelling	oai:doaj.org-article:f7fc9c2706604e96b57a413f8bea1e2e2021-11-11T15:37:23ZModel Predictive Control for Autonomous Driving Vehicles10.3390/electronics102125932079-9292https://doaj.org/article/f7fc9c2706604e96b57a413f8bea1e2e2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2593https://doaj.org/toc/2079-9292The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.Trieu Minh VuReza MoezziJindrich CyrusJaroslav HlavaMDPI AGarticletrajectory trackingnonlinear model predictive controlhard and softened constraintsoptimal control actiontracking errorElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2593, p 2593 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	trajectory tracking nonlinear model predictive control hard and softened constraints optimal control action tracking error Electronics TK7800-8360
spellingShingle	trajectory tracking nonlinear model predictive control hard and softened constraints optimal control action tracking error Electronics TK7800-8360 Trieu Minh Vu Reza Moezzi Jindrich Cyrus Jaroslav Hlava Model Predictive Control for Autonomous Driving Vehicles
description	The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.
format	article
author	Trieu Minh Vu Reza Moezzi Jindrich Cyrus Jaroslav Hlava
author_facet	Trieu Minh Vu Reza Moezzi Jindrich Cyrus Jaroslav Hlava
author_sort	Trieu Minh Vu
title	Model Predictive Control for Autonomous Driving Vehicles
title_short	Model Predictive Control for Autonomous Driving Vehicles
title_full	Model Predictive Control for Autonomous Driving Vehicles
title_fullStr	Model Predictive Control for Autonomous Driving Vehicles
title_full_unstemmed	Model Predictive Control for Autonomous Driving Vehicles
title_sort	model predictive control for autonomous driving vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f7fc9c2706604e96b57a413f8bea1e2e
work_keys_str_mv	AT trieuminhvu modelpredictivecontrolforautonomousdrivingvehicles AT rezamoezzi modelpredictivecontrolforautonomousdrivingvehicles AT jindrichcyrus modelpredictivecontrolforautonomousdrivingvehicles AT jaroslavhlava modelpredictivecontrolforautonomousdrivingvehicles
_version_	1718434897321787392

Model Predictive Control for Autonomous Driving Vehicles

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