A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control

Sensors

Chao Huang¹  Wenqi Fang²  Wenfei Li³  Huiyun Li³  Zhiheng Yang³  Zhejun Huang³  Jia Liu³ 

[1] Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China;Research Center of Digital Intelligence Technology, Nanhu Lab, Jiaxing 314033, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;

关键词: autonomous driving;    trajectory tracking;    real-time control;    model predictive control;   

DOI  :  10.3390/s21217165

来源: DOAJ

【 摘 要 】

Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period.

【 授权许可】

Unknown