Speed-Varying Path Tracking Based on Model Predictive Control for Autonomous Vehicles

In order to improve autonomous vehicles path-tracking accuracy and stability, a lateral–longitudinal coordination path-tracking control method is proposed. The proposed coordination control consists of path-tracking control and speed tracking control. First, the desired safety speed is planned according to the known road curvature and adhesion coefficient in order to prevent the tire force saturation. Based on the three-degree-of-freedom (3DOF) vehicle dynamic model and the preview tracking error model, model predictive control (MPC) theory is adopted to design the speed-varying vehicle path-tracking controller. Then, the quadratic programming (QP) method is used to solve the objective function with constraints, which calculates the steering angle to control the vehicle track the reference path. In addition, a PID speed controller is designed to calculate the torque of each wheel to track the desired speed. Finally, according to the yaw rate error and the vehicle slip angle error, a yaw moment stability controller based on the fuzzy logic control theory is designed to balance the vehicle stability and motility. The simulation results based on a Matlab/Carsim platform show that the coordination path-tracking control method proposed in this paper can effectively improve the vehicle tracking accuracy and the stability on different roads.