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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1149201/full |
| _version_ | 1797884403639123968 |
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| author | Zhaoyong Liu Zhaoyong Liu Yihang Li Weijun Li Zefan Li Haosen Zhang Xiaoqiang Tan Guangqiang Wu |
| author_facet | Zhaoyong Liu Zhaoyong Liu Yihang Li Weijun Li Zefan Li Haosen Zhang Xiaoqiang Tan Guangqiang Wu |
| author_sort | Zhaoyong Liu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T04:07:07Z |
| format | Article |
| id | doaj.art-5030a908c81141f7b4de0ad3c7b42cd1 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-04-10T04:07:07Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-5030a908c81141f7b4de0ad3c7b42cd12023-03-13T04:34:53ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182023-03-011710.3389/fnbot.2023.11492011149201Research on control strategy of vehicle stability based on dynamic stable region regression analysisZhaoyong Liu0Zhaoyong Liu1Yihang Li2Weijun Li3Zefan Li4Haosen Zhang5Xiaoqiang Tan6Guangqiang Wu7School of Automotive Studies, Tongji University, Shanghai, ChinaGlobal Technology Co., Ltd., Nantong, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaSchool of Automotive Studies, Tongji University, Shanghai, ChinaThe 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.https://www.frontiersin.org/articles/10.3389/fnbot.2023.1149201/fullvehicle phase planestability judgementSVRstability controlLTV-MPCAEB |
| spellingShingle | Zhaoyong Liu Zhaoyong Liu Yihang Li Weijun Li Zefan Li Haosen Zhang Xiaoqiang Tan Guangqiang Wu Research on control strategy of vehicle stability based on dynamic stable region regression analysis Frontiers in Neurorobotics vehicle phase plane stability judgement SVR stability control LTV-MPC AEB |
| 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 | vehicle phase plane stability judgement SVR stability control LTV-MPC AEB |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1149201/full |
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