Model Predictive Control with Adaptive Horizon for Vehicle Trajectory Tracking Considering Crosswind Stability

被引:0
作者
Yuan, Zhiqun [1 ,2 ,3 ]
Chen, Yanqiang [1 ]
Chang, Yuxuan [1 ]
Huo, Diansheng [1 ]
Lin, Li [1 ,2 ]
机构
[1] School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen
[2] Fujian Provincial Key Laboratory of Advanced Design and Manufacture for Bus Coach, Xiamen
[3] Fujian Provincial Key Laboratory of Wind Disaster and Wind Engineering, Xiamen
来源
Qiche Gongcheng/Automotive Engineering | 2024年 / 46卷 / 10期
关键词
adaptive horizon; automatic driving; crosswind stability; intelligent car; model predictive control; trajectory tracking;
D O I
10.19562/j.chinasae.qcgc.2024.10.010
中图分类号
学科分类号
摘要
In order to extend the application scenario of model predictive control and improve the trajectory tracking accuracy of intelligent vehicles in extreme wind environment,an adaptive horizon control method considering crosswind stability is proposed. Firstly,taking the process of car overtaking on the sea-crossing bridge as the research object,the crosswind stability analysis model of car overtaking is established by using the coupling method of vehicle aerodynamics and system dynamics. Then,the safety risk model of vehicle lateral motion is established,and the adaptive horizon regulator is designed taking into consideration of lateral motion risk level,vehicle speed and lateral error,so as to realize the dynamic adjustment of prediction horizon and control horizon. Finally,CarSim and Simulink are used to build a joint simulation scenario,and the overtaking trajectory is planned by quintic polynomial to verify the tracking accuracy and robustness of the controller. The results show that compared with the fixed horizon and variable weight model predictive controller,the improved controller can better resist the aerodynamic interference of ' wind-vehicle-bridge' and improve the vehicle trajectory tracking accuracy at a lower real-time cost,with significant improvement in vehicle crosswind stability. © 2024 SAE-China. All rights reserved.
引用
收藏
页码:1829 / 1841and1852
相关论文
共 16 条
  • [11] TANG X Z, SHI L F, WANG B, Et al., Weight adaptive path tracking control for autonomous vehicles based on PSO-BP neural network[J], Sensors, 23, 1, (2023)
  • [12] LIANG B Y, WANG Y P,, LIU X, Et al., Study on crosswind stability control of high-speed vehicle based on sliding mode theory [J], Automotive Engineering, 44, 1, pp. 123-130, (2022)
  • [13] JI J, KHAJEPOUR A, MELEK W W,, Et al., Path planning and tracking for vehicle collision avoidance based on model predictive control with multi-constraints[J], IEEE Transactions on Vehicular Technology, 66, 2, pp. 952-964, (2017)
  • [14] ZHAO F Z, WU Y,, Et al., Model predictive control of soft constraints for autonomous vehicle major lane-changing behavior with time variable model[J], IEEE Access, 9, pp. 89514-89525, (2021)
  • [15] HU C F, ZHAO L X., Overtaking control strategy based on model predictive control with varying horizon for unmanned ground vehicle[J], Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering, 235, 1, pp. 78-92, (2021)
  • [16] WANG H R, WANG Q D, CHEN W W,, Et al., Path tracking based on model predictive control with variable predictive horizon [J], Transactions of the Institute of Measurement and Control, 43, 12, pp. 2676-2688, (2021)