Abstract: Car-like robots often struggle with dynamics modeling owing to the use of nonstandardized parts and the complexity of accurately determining mechanical parameters, particularly tire characteristics like lateral deflection stiffness. Consequently, most current research and applications have relied on kinematic models for control, which frequently lead to inaccuracies and mismatches. These issues result in significant errors between actual and desired paths, causing erratic oscillations in the front wheel angle and angular velocity and adversely affecting the robot’s performance and smoothness. To address these issues, this paper introduces a novel approach: Feed-forward nonlinear model predictive control (FNMPC). This method is built on the principles of inverse kinematics and rolling optimization. Unlike other traditional methods, FNMPC incorporates feed-forward corner information into its predictive model, treating the front wheel angle as an additional dimension. This enhancement allows the model to better predict and correct deviations, thereby improving path-tracking accuracy. Extensive simulations conducted using Simulink and CarSim demonstrated the efficacy of the FNMPC approach. Results indicated that FNMPC significantly reduces the oscillations caused by model inaccuracies while maintaining high tracking accuracy. Specifically, FNMPC managed to keep displacement errors below 0.1106 m and heading errors within 0.1253 radians. When compared with other control strategies such as linear model predictive control, feed-forward linear model predictive control, pure tracking control, and Stanley control, FNMPC consistently demonstrated smaller and less dispersed errors, highlighting its superior performance in handling the complex dynamics of car-like robots. Moreover, FNMPC showed a remarkable improvement over traditional nonlinear model predictive control (NMPC), reducing the absolute cumulative control increment by 67.53% at its maximum values. Experimental validations using a wire-controlled car-like robot further validated FNMPC’s practical benefits. In these tests, the robot under FNMPC control maintained displacement errors within 0.1624 m and heading errors within 0.1138 radians, whereas traditional NMPC lost control of entering curves. In summary, FNMPC presents a substantial advancement in controlling car-like robots, offering enhanced accuracy and smoothness over existing methods. By effectively incorporating feed-forward corner information into the predictive model, FNMPC addresses the inherent challenges in car-like robot dynamics more efficiently. This approach not only improves control performance but also offers a more reliable and accurate method that could enhance the development of car-like robotic systems.