Path Tracking Control of Vehicles Based on Adaptive Model Prediction Control

In order to improve the path tracking accuracy of driverless vehicles at different speed, a fuzzy adaptive model prediction control method was proposed to adjust constant predictive horizon of MPC. Based on the MPC method of 3-DOF vehicle dynamics model, prediction horizon and weight coefficient of the MPC controller could be varied in real time according to the speed and road curvature. With the desired path as the target, the front wheel angle was changed to achieve path tracking. Simulation analysis was performed under the CarSim/Simulink co-simulation environment. Simulation results show that under the condition of satisfying ride comfort and stability of vehicle, the tracking error of the proposed method in the path tracking control is reduced by 30.0%, 29.9% and 14.6% at 36km/h, 72km/h and 108km/h, respectively, which are helpful to path tracking control.