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Intelligent vehicle trajectory tracking lateral control is the key technology to realize intelligent driving. Aiming at the problems that the existing control methods have a small effective control area and poor control effect under cornering conditions or small road adhesion coefficients, a model predictive control (MPC) algorithm is proposed for trajectory tracking control. Based on the two-degree-of-freedom vehicle model, a state space equation is established and discretized to obtain a predictive model that meets the conditions. Then, the corresponding objective function is designed based on the ideal yaw angular velocity under the optimal preview control theory, and the input-output constraints and side-slip angle constraints are designed according to the control and state quantities. The controller model is established in MATLAB / Simulink, and the road model is built in CarSim for joint simulation. Compared with CarSim’s own algorithm, the changes in side acceleration and steering wheel angle are smoother and less oscillating. The maximum lateral deviation and lateral acceleration are also smaller, and the lateral stability of the car is improved. It has better adaptability on pavement with lower adhesion coefficient, and can also maintain control stability. The real vehicle test results are consistent with the simulation results, with a maximum lateral error of 0.44m and a maximum heading angle deviation of 1.43 °. Therefore, the trajectory tracking control algorithm based on model predictive control designed in this paper has significantly improved control accuracy and stability, and can maintain control stability even when the road surface adhesion coefficient is low.

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