Abstract
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.
Highlights
The proposed Model Predictive Control (MPC)-based controller can automatically adjust the velocity according to the information of the reference trajectory
The simulation system consists of a kinematic model of autonomous vehicles and the trajectory tracking controller proposed in this paper
An effective and efficient method for generating a feasible trajectory is of vital importance to meet the requirement of instantaneous control for autonomous driving
Summary
Zhejun Huang 1,2,3 , Huiyun Li 1,2,3, * , Wenfei Li 1,2,3 , Jia Liu 1,2,3 , Chao Huang 4 , Zhiheng Yang 1,2,3 and Wenqi Fang 5. Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen 518055, China
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