In order to improve the accuracy of trajectory tracking of in-wheel motor electric vehicles, a preview time adaptive trajectory tracking method based on iterative algorithm and fuzzy control is proposed. Firstly, based on the vehicle’s three-degree-of-freedom model, the vehicle is controlled to track trajectory based on model predictive control (MPC). The preview step size and sampling period of MPC are adjusted by iterative function and fuzzy controller, respectively. Then, In order to optimize MPC active steering control, a differential torque controller is established to realize the trajectory tracking control of differential torque steering. Finally, Carsim/Simulink co-simulation analysis and real vehicle verification are done. The simulation results show that the controller can complete the trajectory tracking control of the in-wheel motor intelligent vehicle, and the stability and steering performance are good. The controller has good robustness and adaptability according to road adhesion conditions and vehicle speed changes. At the same time, the trajectory tracking accuracy of the MPC controller is better than sliding mode variable structure control (SMC). The real vehicle verification results show that when the real vehicle tracking under different speeds, the adaptive preview time controller designed in this paper has good trajectory tracking performance and stability.

Because the real road conditions are complex and changeable, the vehicle cannot drive normally according to the predetermined trajectory. Therefore, for the four-wheel drive electric vehicle, a trajectory tracking control method of joint planning layer is proposed. First, in the process of vehicle trajectory tracking, the obstacle avoidance trajectory is planned for the dynamic obstacle environment. Then, an MPC trajectory tracking controller with a velocity planning module is designed to reduce the lateral acceleration of the vehicle avoiding obstacles at high speed during trajectory tracking. Finally, CarSim/Simulink co-simulation verification and hardware-in-the-loop (HIL) simulation verification are carried out to verify the performance of the controller. The simulation results show that the trajectory planning module can successfully plan the trajectory of obstacle avoidance, and the improved MPC controller can effectively reduce the lateral acceleration of the target vehicle during trajectory tracking; HIL analysis shows that the MPC trajectory tracker with speed planning module can control the vehicle with good driving stability. Trajectory tracking control under urban road conditions will be considered in future research.