Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment
In order to improve the trajectory smoothness and the accuracy of lane change control, an adaptive control algorithm based on weight coefficient was proposed. According to lane change trajectory constraint conditions, the sixth-order polynomial lane change trajectory applied to intelligent vehicles...
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/16878140211003393 |
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doaj-a2ab40a3458b40f585b5283ec55df65e2021-03-13T04:34:38ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402021-03-011310.1177/16878140211003393Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustmentJunnian Wang0Fei Teng1Jing Li2Liguo Zang3Tianxin Fan4Jiaxu Zhang5Xingyu Wang6State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaSchool of Vehicle and Energy, Yanshan University, Qinhuangdao, ChinaSchool of Automotive & Rail Transit, Nanjing Institute of Technology, Nanjing, ChinaState Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, ChinaIntelligent Network R&D Institute, China FAW Group Co., Ltd., Changchun, ChinaSchool of Automotive & Rail Transit, Nanjing Institute of Technology, Nanjing, ChinaIn order to improve the trajectory smoothness and the accuracy of lane change control, an adaptive control algorithm based on weight coefficient was proposed. According to lane change trajectory constraint conditions, the sixth-order polynomial lane change trajectory applied to intelligent vehicles was constructed. Based on the vehicle model and the model predictive control theory, the time-varying linear variable path vehicle predictive model was derived by combining soft constraint of the side slip angle. Combined with fuzzy control algorithm, the weight coefficient of the deviation of the lateral displacement was dynamically adjusted. Finally, the FMPC (model predictive controller based on fuzzy control) and MPC controller were compared and analyzed by co-simulation of CarSim and Simulink under different speeds. The simulation results show that the designed FMPC controller can track the lane change trajectory better, and the controller has better robustness when the vehicle changes lanes at different speeds.https://doi.org/10.1177/16878140211003393 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junnian Wang Fei Teng Jing Li Liguo Zang Tianxin Fan Jiaxu Zhang Xingyu Wang |
spellingShingle |
Junnian Wang Fei Teng Jing Li Liguo Zang Tianxin Fan Jiaxu Zhang Xingyu Wang Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment Advances in Mechanical Engineering |
author_facet |
Junnian Wang Fei Teng Jing Li Liguo Zang Tianxin Fan Jiaxu Zhang Xingyu Wang |
author_sort |
Junnian Wang |
title |
Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment |
title_short |
Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment |
title_full |
Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment |
title_fullStr |
Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment |
title_full_unstemmed |
Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment |
title_sort |
intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2021-03-01 |
description |
In order to improve the trajectory smoothness and the accuracy of lane change control, an adaptive control algorithm based on weight coefficient was proposed. According to lane change trajectory constraint conditions, the sixth-order polynomial lane change trajectory applied to intelligent vehicles was constructed. Based on the vehicle model and the model predictive control theory, the time-varying linear variable path vehicle predictive model was derived by combining soft constraint of the side slip angle. Combined with fuzzy control algorithm, the weight coefficient of the deviation of the lateral displacement was dynamically adjusted. Finally, the FMPC (model predictive controller based on fuzzy control) and MPC controller were compared and analyzed by co-simulation of CarSim and Simulink under different speeds. The simulation results show that the designed FMPC controller can track the lane change trajectory better, and the controller has better robustness when the vehicle changes lanes at different speeds. |
url |
https://doi.org/10.1177/16878140211003393 |
work_keys_str_mv |
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