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Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions

This paper presents an innovative electronically controlled suspension system installed on a real car and used as a test bench. The proposed setup relies on a sensor network that acquires a large real-time dataset collecting the car vibrations and the car trim and, through a new controller based on...

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Detalles Bibliográficos
Autores principales: Pepe, Gianluca, Roveri, Nicola, Carcaterra, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679211/
https://www.ncbi.nlm.nih.gov/pubmed/31336758
http://dx.doi.org/10.3390/s19143062
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author Pepe, Gianluca
Roveri, Nicola
Carcaterra, Antonio
author_facet Pepe, Gianluca
Roveri, Nicola
Carcaterra, Antonio
author_sort Pepe, Gianluca
collection PubMed
description This paper presents an innovative electronically controlled suspension system installed on a real car and used as a test bench. The proposed setup relies on a sensor network that acquires a large real-time dataset collecting the car vibrations and the car trim and, through a new controller based on a recently proposed theory developed by the authors, makes use of adjustable semi-active magneto-rheological dampers. A BMW series 1 is equipped with such an integrated sensors-controller-actuators device and an extensive test campaign, in real driving conditions, is carried out to evaluate its performance. Thanks to its strategy, the new plant enhances, at once, both comfort and drivability of the car, as field experiments show. A benchmark analysis is performed, comparing the performance of the new control system with the ones of traditional semi-active suspensions, such as skyhook devices: the comparison shows very good results for the proposed solution.
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spelling pubmed-66792112019-08-19 Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions Pepe, Gianluca Roveri, Nicola Carcaterra, Antonio Sensors (Basel) Article This paper presents an innovative electronically controlled suspension system installed on a real car and used as a test bench. The proposed setup relies on a sensor network that acquires a large real-time dataset collecting the car vibrations and the car trim and, through a new controller based on a recently proposed theory developed by the authors, makes use of adjustable semi-active magneto-rheological dampers. A BMW series 1 is equipped with such an integrated sensors-controller-actuators device and an extensive test campaign, in real driving conditions, is carried out to evaluate its performance. Thanks to its strategy, the new plant enhances, at once, both comfort and drivability of the car, as field experiments show. A benchmark analysis is performed, comparing the performance of the new control system with the ones of traditional semi-active suspensions, such as skyhook devices: the comparison shows very good results for the proposed solution. MDPI 2019-07-11 /pmc/articles/PMC6679211/ /pubmed/31336758 http://dx.doi.org/10.3390/s19143062 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pepe, Gianluca
Roveri, Nicola
Carcaterra, Antonio
Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions
title Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions
title_full Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions
title_fullStr Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions
title_full_unstemmed Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions
title_short Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions
title_sort experimenting sensors network for innovative optimal control of car suspensions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679211/
https://www.ncbi.nlm.nih.gov/pubmed/31336758
http://dx.doi.org/10.3390/s19143062
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