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Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation
In this study, an intelligent vehicle (IV) path tracking control method based on curvature optimisation is proposed to reduce the comprehensive performance conflict of the system. This system conflict is caused by the mutual restriction between the path tracking accuracy and the body stability durin...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221627/ https://www.ncbi.nlm.nih.gov/pubmed/37430632 http://dx.doi.org/10.3390/s23104719 |
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author | Ye, Qing Gao, Chaojun Zhang, Yao Sun, Zeyu Wang, Ruochen Chen, Long |
author_facet | Ye, Qing Gao, Chaojun Zhang, Yao Sun, Zeyu Wang, Ruochen Chen, Long |
author_sort | Ye, Qing |
collection | PubMed |
description | In this study, an intelligent vehicle (IV) path tracking control method based on curvature optimisation is proposed to reduce the comprehensive performance conflict of the system. This system conflict is caused by the mutual restriction between the path tracking accuracy and the body stability during the movement of the intelligent automobile. First, the working principle of the new IV path tracking control algorithm is briefly introduced. Then, a three-degrees-of-freedom vehicle dynamics model and a preview error model considering vehicle roll are established. In addition, a path tracking control method based on curvature optimisation is designed to solve the deterioration of vehicle stability even when the path tracking accuracy of the IV is improved. Finally, the effectiveness of the IV path tracking control system is validated through simulations and the Hardware in the Loop (HIL) test with various conditions forms. Results clearly show that the optimisation amplitude of the IV lateral deviation is up to 84.10%, and the stability is improved by approximately 2% under the v(x) = 10 m/s and ρ = 0.15 m(−1) condition; the optimisation amplitude of the lateral deviation is up to 66.80%, and the stability is improved by approximately 4% under the v(x) = 10 m/s and ρ = 0.2 m(−1) condition; the body stability is improved by 20–30% under the v(x) = 15 m/s and ρ = 0.15 m(−1) condition, and the boundary conditions of body stability are triggered. The curvature optimisation controller can effectively improve the tracking accuracy of the fuzzy sliding mode controller. The body stability constraint can also ensure the smooth running of the vehicle in the optimisation process. |
format | Online Article Text |
id | pubmed-10221627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102216272023-05-28 Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation Ye, Qing Gao, Chaojun Zhang, Yao Sun, Zeyu Wang, Ruochen Chen, Long Sensors (Basel) Article In this study, an intelligent vehicle (IV) path tracking control method based on curvature optimisation is proposed to reduce the comprehensive performance conflict of the system. This system conflict is caused by the mutual restriction between the path tracking accuracy and the body stability during the movement of the intelligent automobile. First, the working principle of the new IV path tracking control algorithm is briefly introduced. Then, a three-degrees-of-freedom vehicle dynamics model and a preview error model considering vehicle roll are established. In addition, a path tracking control method based on curvature optimisation is designed to solve the deterioration of vehicle stability even when the path tracking accuracy of the IV is improved. Finally, the effectiveness of the IV path tracking control system is validated through simulations and the Hardware in the Loop (HIL) test with various conditions forms. Results clearly show that the optimisation amplitude of the IV lateral deviation is up to 84.10%, and the stability is improved by approximately 2% under the v(x) = 10 m/s and ρ = 0.15 m(−1) condition; the optimisation amplitude of the lateral deviation is up to 66.80%, and the stability is improved by approximately 4% under the v(x) = 10 m/s and ρ = 0.2 m(−1) condition; the body stability is improved by 20–30% under the v(x) = 15 m/s and ρ = 0.15 m(−1) condition, and the boundary conditions of body stability are triggered. The curvature optimisation controller can effectively improve the tracking accuracy of the fuzzy sliding mode controller. The body stability constraint can also ensure the smooth running of the vehicle in the optimisation process. MDPI 2023-05-12 /pmc/articles/PMC10221627/ /pubmed/37430632 http://dx.doi.org/10.3390/s23104719 Text en © 2023 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 Ye, Qing Gao, Chaojun Zhang, Yao Sun, Zeyu Wang, Ruochen Chen, Long Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation |
title | Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation |
title_full | Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation |
title_fullStr | Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation |
title_full_unstemmed | Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation |
title_short | Intelligent Vehicle Path Tracking Control Method Based on Curvature Optimisation |
title_sort | intelligent vehicle path tracking control method based on curvature optimisation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221627/ https://www.ncbi.nlm.nih.gov/pubmed/37430632 http://dx.doi.org/10.3390/s23104719 |
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