Intelligent Vehicle Trajectory Tracking Based on Multi-parameter Adaptive Optimization

被引：0

作者：

Jin H. ^{[1
]}

Lu K. ^{[1
]}

机构：

[1] School of Mechanical Engineering, Beijing Institute of Technology, Beijing

来源：

Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2023年 / 36卷 / 05期

关键词：

automotive engineering; feedforward neural network; intelligent vehicle; particle swarm optimization algorithm; road adhesion coefficient identification; trajectory tracking control;

D O I：

10.19721/j.cnki.1001-7372.2023.05.022

中图分类号：

学科分类号：

摘要：

Intelligent vehicles travel under various conditions. In this study, to improve the tracking accuracy, calculation speed, and vehicle stability under different working conditions, a parameter adaptive model predictive control algorithm is proposed based on different vehicle speeds and road adhesion coefficients. Vehicle stability control is added based on a linear time-varying MPC, and two control strategies are developed based on the road adhesion coefficient. On a high-adhesion-coefficient road, the prediction and control horizons arc optimized for different vehicle speeds. On a road with a low adhesion coefficient, stability control is implemented, and weight parameters are optimized based on the improved particle swarm optimization algorithm. On the premise of ensuring the tracking accuracy of the algorithm and vehicle stability,these two strategies increase computational speed. Further, a road-friction recognition algorithm based on a feedforward neural network is designed to determine the road-surface adhesion coefficient of the parameter-adaptive trajectory tracking algorithm. CarSim-Simulink is used for the co-simulation. The results reveal that the average absolute percentage error of the road recognition algorithm is 12. 77%, which is sufficient to satisfy the requirements of the multiparameter adaptive trajectory tracking algorithm. Compared with the traditional linear time-varying MPC tracking algorithm, on roads with high and low road adhesion coefficients, the transverse mean absolute error of the multiparameter adaptive trajectory tracking algorithm is reduced by 20. 7% and 24. 6% at low speeds, whereas it is decreases by 66. 2% and 50. 7% at high speeds, respectively. The computation time of the algorithm is reduced by 40. 2%. Thus, the vehicle stability is guaranteed, and the computation time is reduced. In this study, some parameters of the trajectory tracking algorithm are optimized for different vehicle speeds and road adhesion coefficients, and the adaptive prediction horizon, control horizon, and weight parameters are used to cooperatively optimize the control, thus providing a new idea for the study of trajectory tracking control under complex working conditions. © 2023 Xi'an Highway University. All rights reserved.

引用

页码：260 / 272

页数：12

共 22 条

[1] ROKONUZZAMAN M, MOHAJER N, NAHAVANDI S, Et al., Review and performance evaluation of path tracking controllers of autonomous vehicles, IET Intelligent Transport Systems, 15, 5, pp. 646-670, (2021)
[2] ZHAI L, WANG C, HOU Y, Et al., MPC-hased integrated control of trajectory tracking and handling stability for intelligent driving vehicle driven by four huh motor, IEEE Transactions on Vehicular Technology, 71, 3, pp. 2668-2680, (2022)
[3] CHU D, LI H, ZHAO C, Et al., Trajectory tracking of autonomous vehicle based on model predictive control with PID feedback [J], IEEE Transactions on Intelligent Transportation Systems, 24, 2, pp. 2239-2250, (2022)
[4] CHEN S, CHEN H, PLETTA A, Et al., Coupled lateral and longitudinal control for trajectory tracking, lateral stability, and rollover prevention of high-speed automated vehicles using minimum-time model predictive control, 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 1-10, (2021)
[5] LIANG Zhong-chao, ZHANG Huan, ZHAO Jmg, Et al., Trajectory tracking control of unmanned vehicles based on adaptive MFC [J], Journal of Northeastern University (Natural Science), 41, 6, pp. 835-840, (2020)
[6] CHEN Y, HU C, WANG J., Human-centered trajectory tracking control for autonomous vehicles with driver cut-in behavior prediction [J], IEEE Transactions on Vehicular Technology, 68, 9, pp. 8461-8471, (2019)
[7] LI Z, ZHAO P, JIANG C, Et al., A learning-based model predictive trajectory planning controller for automated driving in unstructured dynamic environments [J], IEEE Transactions on Vehicular Technology, 71, 6, pp. 5944-5959, (2022)
[8] GRIGORESCU S, TRASNEA B, COCIAS T, Et al., A survey of deep learning techniques for autonomous driving [J], Journal of Field Robotics, 37, 3, pp. 362-386, (2020)
[9] WANG J, TENG F, LI J, Et al., Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment [J], Advances in Mechanical Engineering, 13, 3, pp. 1-16, (2021)
[10] TANG Chuan-ym, ZHAO Yi-feng, ZHAO Ya-feng, Et al., Research on the trajectory tracking control method of intelligent vehicles [J], Journal of Northeastern University (Natural Science), 41, 9, pp. 1297-1303, (2020)

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