Abstract: Car-like robots are mobile robots commonly used in manufacturing and warehousing. This type of robot has a mechanical structure similar to that of an unmanned vehicle, which uses the front wheels as steering structures. However, this type of robot has characteristics that significantly influence path tracking control relative to unmanned vehicles, such as a larger magnitude of reference path curvature, a smaller range of system constraints, and a lower degree of part standardization. Consequently, several research efforts dedicated to path tracking for car-like robots have emerged. Among the path-tracking control methods for car-like robots, model predictive control (MPC) has a tremendous advantage in dealing with system constraints. However, the existing nonlinear model predictive control (Nonlinear MPC, NMPC) has inferior real-time performance, and the linear model predictive control (LMPC) has poor accuracy. Therefore, a path-tracking control method for car-like robots with high accuracy and superior real-time performance needs to be developed. Because the reason for the low accuracy of LMPC in paths with significant curvature changes is that the response of LMPC is not timely after the curvature change, the idea of combining LMPC and feed-forward information is adopted. The basis of path-tracking controller for car-like robots is the LMPC. The reference front wheel angle at the reference path point in front of the car-like robot is obtained as a feed-forward signal through an inverse kinematic model. A feed-forward optimization objective function that conforms to the LMPC architecture is designed, and a feed-forward model predictive control (FMPC) is proposed by combining the feed-forward optimization objective function with the LMPC. The FMPC is tested by joint simulation using MATLAB and CarSim. The FMPC has high accuracy, the absolute value of the displacement error in all of the simulation results does not exceed 0.1110 m, and the absolute value of the heading error does not exceed 0.1177 rad. The accuracy of the FMPC is comparable to that of the NMPC under the same conditions, and the errors of LMPC, feed-forward control, and Stanley control are dispersed under these conditions. The FMPC also has superior real-time performance, and the solving time in each control period does not exceed 4.31 ms. Under the same conditions, the FMPC is comparable to the LMPC in terms of real-time performance and can reduce the maximum value of the solving time in each control cycle by 80.68% and the average value by 65.14% compared with the NMPC. The FMPC can also ensure that the control variables are within the system constraints and are less affected by positioning errors.