Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles

Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially i...

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Autores principales:	Man-Wen Tian, Shu-Rong Yan, Ardashir Mohammadzadeh, Jafar Tavoosi, Saleh Mobayen, Rabia Safdar, Wudhichai Assawinchaichote, Mai The Vu, Anton Zhilenkov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fuzzy system autonomous vehicles machine learning path-following stability Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/17b2fc334b4f4041973a4f19bb784c0f
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spelling	oai:doaj.org-article:17b2fc334b4f4041973a4f19bb784c0f2021-11-11T18:17:28ZStability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles10.3390/math92127422227-7390https://doaj.org/article/17b2fc334b4f4041973a4f19bb784c0f2021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2742https://doaj.org/toc/2227-7390Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance.Man-Wen TianShu-Rong YanArdashir MohammadzadehJafar TavoosiSaleh MobayenRabia SafdarWudhichai AssawinchaichoteMai The VuAnton ZhilenkovMDPI AGarticlefuzzy systemautonomous vehiclesmachine learningpath-followingstabilityMathematicsQA1-939ENMathematics, Vol 9, Iss 2742, p 2742 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fuzzy system autonomous vehicles machine learning path-following stability Mathematics QA1-939
spellingShingle	fuzzy system autonomous vehicles machine learning path-following stability Mathematics QA1-939 Man-Wen Tian Shu-Rong Yan Ardashir Mohammadzadeh Jafar Tavoosi Saleh Mobayen Rabia Safdar Wudhichai Assawinchaichote Mai The Vu Anton Zhilenkov Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles
description	Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance.
format	article
author	Man-Wen Tian Shu-Rong Yan Ardashir Mohammadzadeh Jafar Tavoosi Saleh Mobayen Rabia Safdar Wudhichai Assawinchaichote Mai The Vu Anton Zhilenkov
author_facet	Man-Wen Tian Shu-Rong Yan Ardashir Mohammadzadeh Jafar Tavoosi Saleh Mobayen Rabia Safdar Wudhichai Assawinchaichote Mai The Vu Anton Zhilenkov
author_sort	Man-Wen Tian
title	Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles
title_short	Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles
title_full	Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles
title_fullStr	Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles
title_full_unstemmed	Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles
title_sort	stability of interval type-3 fuzzy controllers for autonomous vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/17b2fc334b4f4041973a4f19bb784c0f
work_keys_str_mv	AT manwentian stabilityofintervaltype3fuzzycontrollersforautonomousvehicles AT shurongyan stabilityofintervaltype3fuzzycontrollersforautonomousvehicles AT ardashirmohammadzadeh stabilityofintervaltype3fuzzycontrollersforautonomousvehicles AT jafartavoosi stabilityofintervaltype3fuzzycontrollersforautonomousvehicles AT salehmobayen stabilityofintervaltype3fuzzycontrollersforautonomousvehicles AT rabiasafdar stabilityofintervaltype3fuzzycontrollersforautonomousvehicles AT wudhichaiassawinchaichote stabilityofintervaltype3fuzzycontrollersforautonomousvehicles AT maithevu stabilityofintervaltype3fuzzycontrollersforautonomousvehicles AT antonzhilenkov stabilityofintervaltype3fuzzycontrollersforautonomousvehicles
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Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles

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