This study proposes an innovative combined control method that integrates feedforward and feedback control to improve the overall performance of the trajectory tracking control system for intelligent vehicles. In the feedforward control component, the initial control input is determined based on the system’s kinematic model and a predetermined reference trajectory, providing guidance during execution. In the feedback control component, an event-triggered model predictive control based on the proximal policy optimization algorithm is introduced to generate the feedback control input. The event-triggered model predictive control algorithm using proximal policy optimization algorithm does not need to recompute the control sequence at every time step, it only recalculates the control sequence upon receiving a trigger signal. This mechanism effectively reduces unnecessary computational steps, thereby significantly optimizing computation time and conserving computational resources. By combining the feedforward and feedback control inputs, this method achieves an efficient and robust control strategy. Simulation experiments validate that this approach not only improves the system’s response speed but also significantly enhances control accuracy.