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Research on control strategy of vehicle stability based on dynamic stable region regression analysis
The intervention time of stability control system is determined by stability judgment, which is the basis of vehicle stability control. According to the different working conditions of the vehicle, we construct the phase plane of the vehicle's sideslip angle and sideslip angular velocity, and e...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042292/ https://www.ncbi.nlm.nih.gov/pubmed/36994073 http://dx.doi.org/10.3389/fnbot.2023.1149201 |
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author | Liu, Zhaoyong Li, Yihang Li, Weijun Li, Zefan Zhang, Haosen Tan, Xiaoqiang Wu, Guangqiang |
author_facet | Liu, Zhaoyong Li, Yihang Li, Weijun Li, Zefan Zhang, Haosen Tan, Xiaoqiang Wu, Guangqiang |
author_sort | Liu, Zhaoyong |
collection | PubMed |
description | The intervention time of stability control system is determined by stability judgment, which is the basis of vehicle stability control. According to the different working conditions of the vehicle, we construct the phase plane of the vehicle's sideslip angle and sideslip angular velocity, and establish the sample dataset of the stable region of the different phase planes. To reduce the complexity of phase plane stable region division and avoid large amount of data, we established the support vector regression (SVR) model, and realized the automatic regression of dynamic stable region. The testing of the test set shows that the model established in this paper has strong generalization ability. We designed a direct yaw-moment control (DYC) stability controller based on linear time-varying model predictive control (LTV-MPC). The influence of key factors such as centroid position and road adhesion coefficient on the stable region is analyzed through phase diagram. The effectiveness of the stability judgment and control algorithm is verified by simulation tests. |
format | Online Article Text |
id | pubmed-10042292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100422922023-03-28 Research on control strategy of vehicle stability based on dynamic stable region regression analysis Liu, Zhaoyong Li, Yihang Li, Weijun Li, Zefan Zhang, Haosen Tan, Xiaoqiang Wu, Guangqiang Front Neurorobot Neuroscience The intervention time of stability control system is determined by stability judgment, which is the basis of vehicle stability control. According to the different working conditions of the vehicle, we construct the phase plane of the vehicle's sideslip angle and sideslip angular velocity, and establish the sample dataset of the stable region of the different phase planes. To reduce the complexity of phase plane stable region division and avoid large amount of data, we established the support vector regression (SVR) model, and realized the automatic regression of dynamic stable region. The testing of the test set shows that the model established in this paper has strong generalization ability. We designed a direct yaw-moment control (DYC) stability controller based on linear time-varying model predictive control (LTV-MPC). The influence of key factors such as centroid position and road adhesion coefficient on the stable region is analyzed through phase diagram. The effectiveness of the stability judgment and control algorithm is verified by simulation tests. Frontiers Media S.A. 2023-03-13 /pmc/articles/PMC10042292/ /pubmed/36994073 http://dx.doi.org/10.3389/fnbot.2023.1149201 Text en Copyright © 2023 Liu, Li, Li, Li, Zhang, Tan and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Liu, Zhaoyong Li, Yihang Li, Weijun Li, Zefan Zhang, Haosen Tan, Xiaoqiang Wu, Guangqiang Research on control strategy of vehicle stability based on dynamic stable region regression analysis |
title | Research on control strategy of vehicle stability based on dynamic stable region regression analysis |
title_full | Research on control strategy of vehicle stability based on dynamic stable region regression analysis |
title_fullStr | Research on control strategy of vehicle stability based on dynamic stable region regression analysis |
title_full_unstemmed | Research on control strategy of vehicle stability based on dynamic stable region regression analysis |
title_short | Research on control strategy of vehicle stability based on dynamic stable region regression analysis |
title_sort | research on control strategy of vehicle stability based on dynamic stable region regression analysis |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10042292/ https://www.ncbi.nlm.nih.gov/pubmed/36994073 http://dx.doi.org/10.3389/fnbot.2023.1149201 |
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