Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain

Advancements have been made in the field of vehicle dynamics, improving the handling and safety of the vehicle through control systems such as the Antilock Braking System (ABS). An ABS enhances the braking performance and steerability of a vehicle under severe braking conditions by preventing wheel...

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Autores principales:	de Abreu Ricardo, Botha Theunis R., Hamersma Herman A.
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/88cd59e2224143cd9e06306c9fe31bc4
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id	oai:doaj.org-article:88cd59e2224143cd9e06306c9fe31bc4
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spelling	oai:doaj.org-article:88cd59e2224143cd9e06306c9fe31bc42021-12-02T17:13:35ZModel-free Intelligent Control for Antilock Braking Systems on Rough Terrain2261-236X10.1051/matecconf/202134700018https://doaj.org/article/88cd59e2224143cd9e06306c9fe31bc42021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/16/matecconf_sacam21_00018.pdfhttps://doaj.org/toc/2261-236XAdvancements have been made in the field of vehicle dynamics, improving the handling and safety of the vehicle through control systems such as the Antilock Braking System (ABS). An ABS enhances the braking performance and steerability of a vehicle under severe braking conditions by preventing wheel lockup. However, its performance degrades on rough terrain resulting in an increased wheel lockup and stopping distance compared to without. This is largely as a result of noisy measurements, and un-modelled dynamics that occur as a result of the vertical and torsional excitation experienced over rough terrain. Therefore, it is proposed that a model-free intelligent technique, which may adapt to these dynamics, be used to overcome this problem. The Double Deep Q-learning (DDQN) technique in conjunction with a Temporal Convolutional Network (TCN) is proposed as the intelligent control algorithm, and straight line braking simulations are performed using a single tyre model, with tyre characteristics approximated by the LuGre tyre model. The rough terrain is modelled after the measured Belgian paving with the normal forces at the tyre contact patch approximated using FTire in ADAMS. Comparisons are drawn against the Bosch algorithm, and results show that the intelligent control approach achieves lateral stability by preventing wheel lockup whilst braking over rough terrain, without deteriorating the stopping distance.de Abreu RicardoBotha Theunis R.Hamersma Herman A.EDP SciencesarticleEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 347, p 00018 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 de Abreu Ricardo Botha Theunis R. Hamersma Herman A. Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain
description	Advancements have been made in the field of vehicle dynamics, improving the handling and safety of the vehicle through control systems such as the Antilock Braking System (ABS). An ABS enhances the braking performance and steerability of a vehicle under severe braking conditions by preventing wheel lockup. However, its performance degrades on rough terrain resulting in an increased wheel lockup and stopping distance compared to without. This is largely as a result of noisy measurements, and un-modelled dynamics that occur as a result of the vertical and torsional excitation experienced over rough terrain. Therefore, it is proposed that a model-free intelligent technique, which may adapt to these dynamics, be used to overcome this problem. The Double Deep Q-learning (DDQN) technique in conjunction with a Temporal Convolutional Network (TCN) is proposed as the intelligent control algorithm, and straight line braking simulations are performed using a single tyre model, with tyre characteristics approximated by the LuGre tyre model. The rough terrain is modelled after the measured Belgian paving with the normal forces at the tyre contact patch approximated using FTire in ADAMS. Comparisons are drawn against the Bosch algorithm, and results show that the intelligent control approach achieves lateral stability by preventing wheel lockup whilst braking over rough terrain, without deteriorating the stopping distance.
format	article
author	de Abreu Ricardo Botha Theunis R. Hamersma Herman A.
author_facet	de Abreu Ricardo Botha Theunis R. Hamersma Herman A.
author_sort	de Abreu Ricardo
title	Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain
title_short	Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain
title_full	Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain
title_fullStr	Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain
title_full_unstemmed	Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain
title_sort	model-free intelligent control for antilock braking systems on rough terrain
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/88cd59e2224143cd9e06306c9fe31bc4
work_keys_str_mv	AT deabreuricardo modelfreeintelligentcontrolforantilockbrakingsystemsonroughterrain AT bothatheunisr modelfreeintelligentcontrolforantilockbrakingsystemsonroughterrain AT hamersmahermana modelfreeintelligentcontrolforantilockbrakingsystemsonroughterrain
_version_	1718381329833263104

Model-free Intelligent Control for Antilock Braking Systems on Rough Terrain

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