In this paper，a model predictive control （MPC） method integrating feed-forward and state feedback is proposed for the problem of accurate path tracking for intelligent vehicles with dynamic constraints. Firstly， the MPC path tracking base model is established according to the vehicle two-degree-of-freedom model， and then， the modeled steady-state perturbations generated by the road curvature changes on the system in the base model are considered and designed to be eliminated by feed-forward control （FFC）； Furthermore， the proportional integral derivative （PID） controller is used to regulate the system error state feedback； Meanwhile， the model predictive optimal regulation control law （MPC-FF-PID） is verified by integrating the feed-forward and state feedback corner inputs. Finally， the effectiveness of the proposed algorithm is confirmed based on MATLAB/Simulink and Carsim platforms， and a real vehicle test is carried out in the low-speed scenario in the park based on the intelligent driving real vehicle platform， and the maximum lateral and heading errors are 0.128 7 m and 0.063 9 rad， respectively， indicating that the proposed algorithm has higher tracking accuracy and safety.