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Automotive Engineering ›› 2022, Vol. 44 ›› Issue (5): 664-674.doi: 10.19562/j.chinasae.qcgc.2022.05.003

Special Issue: 智能网联汽车技术专题-规划&控制2022年

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Research on Model Predictive Control of Autonomous Vehicle Path Tracking Under Complex Road Condition

Jun Li1,Wenxing Wan1,Sanqiang Hao2,Wu Qin1,2(),Feifei Liu1   

  1. 1.School of Mechatronics & Vehicle Engineering,East China Jiaotong University,Nanchang  330000
    2.Jian Xin Zhao’s Technology Co. Ltd. ,Ningbo  315000
  • Received:2021-11-16 Revised:2021-12-13 Online:2022-05-25 Published:2022-05-27
  • Contact: Wu Qin E-mail:qw@ecjtu.edu.cn

Abstract:

To improve the path tracking accuracy, driving stability and safety of autonomous vehicle under complex road conditions of right angle turn, continuous curve and arc curve, an improved model predictive control (MPC) algorithm is proposed. The salient feature of the improved MPC is that the maximum longitudinal speed of vehicle without sliding on the flat road is determined by the curvature of the travel path, that is, the longitudinal speed of the vehicle is not assumed to be constant. Based on model predictive control, the vehicle kinematics model is established. Speed and front wheel angle are set as constraints. And position deviation and control increment are designed as the objective function to obtain the optimal front wheel angle and driving speed. Finally, with the experimental platform and test site provided by a new energy vehicle company, the path tracking effect of the improved MPC under complex road conditions and the model predictive control algorithm with constant longitudinal speed is compared and analyzed. The experimental results verify the effectiveness and superiority of the improved MPC, and indicate that the accuracy of the path tracking, the driving stability and the safety of vehicle are guaranteed.

Key words: autonomous vehicle, path tracking, model predictive control, complex road condition