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Optimal H(∞) Control for Lateral Dynamics of Autonomous Vehicles

This paper presents the design and validation of a model-based [Formula: see text] vehicle lateral controller for autonomous vehicles in a simulation environment. The controller was designed so that the position and orientation tracking errors are minimized and so that the vehicle is able to follow...

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Detalles Bibliográficos
Autores principales: Gagliardi, Gianfranco, Lupia, Marco, Cario, Gianni, Casavola, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231833/
https://www.ncbi.nlm.nih.gov/pubmed/34199181
http://dx.doi.org/10.3390/s21124072
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author Gagliardi, Gianfranco
Lupia, Marco
Cario, Gianni
Casavola, Alessandro
author_facet Gagliardi, Gianfranco
Lupia, Marco
Cario, Gianni
Casavola, Alessandro
author_sort Gagliardi, Gianfranco
collection PubMed
description This paper presents the design and validation of a model-based [Formula: see text] vehicle lateral controller for autonomous vehicles in a simulation environment. The controller was designed so that the position and orientation tracking errors are minimized and so that the vehicle is able to follow a trajectory computed in real-time by exploiting proper video-processing and lane-detection algorithms. From a computational point of view, the controller is obtained by solving a suitable LMI optimization problem and ensures that the closed-loop system is robust with respect to variations in the vehicle’s longitudinal speed. In order to show the effectiveness of the proposed control strategy, simulations have been undertaken by taking advantage of a co-simulation environment jointly developed in Matlab/Simulink © and Carsim 8 ©. The simulation activity shows that the proposed control approach allows for good control performance to be achieved.
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spelling pubmed-82318332021-06-26 Optimal H(∞) Control for Lateral Dynamics of Autonomous Vehicles Gagliardi, Gianfranco Lupia, Marco Cario, Gianni Casavola, Alessandro Sensors (Basel) Article This paper presents the design and validation of a model-based [Formula: see text] vehicle lateral controller for autonomous vehicles in a simulation environment. The controller was designed so that the position and orientation tracking errors are minimized and so that the vehicle is able to follow a trajectory computed in real-time by exploiting proper video-processing and lane-detection algorithms. From a computational point of view, the controller is obtained by solving a suitable LMI optimization problem and ensures that the closed-loop system is robust with respect to variations in the vehicle’s longitudinal speed. In order to show the effectiveness of the proposed control strategy, simulations have been undertaken by taking advantage of a co-simulation environment jointly developed in Matlab/Simulink © and Carsim 8 ©. The simulation activity shows that the proposed control approach allows for good control performance to be achieved. MDPI 2021-06-13 /pmc/articles/PMC8231833/ /pubmed/34199181 http://dx.doi.org/10.3390/s21124072 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gagliardi, Gianfranco
Lupia, Marco
Cario, Gianni
Casavola, Alessandro
Optimal H(∞) Control for Lateral Dynamics of Autonomous Vehicles
title Optimal H(∞) Control for Lateral Dynamics of Autonomous Vehicles
title_full Optimal H(∞) Control for Lateral Dynamics of Autonomous Vehicles
title_fullStr Optimal H(∞) Control for Lateral Dynamics of Autonomous Vehicles
title_full_unstemmed Optimal H(∞) Control for Lateral Dynamics of Autonomous Vehicles
title_short Optimal H(∞) Control for Lateral Dynamics of Autonomous Vehicles
title_sort optimal h(∞) control for lateral dynamics of autonomous vehicles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8231833/
https://www.ncbi.nlm.nih.gov/pubmed/34199181
http://dx.doi.org/10.3390/s21124072
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