The model predictive control method is currently very successful and is used extensively in crane systems to follow the planned trajectory and constrain the vibration angles during the movement. However, this method’s computational process is rather complex, increasing the microcontroller’s processing and storage volume and lowering the control speed. The global stabilization of the closed-loop system was ensured when the LMPC take the stability condition from the Lyapunov function as a constraint. Furthermore, employing this method to demonstrate the system's stability is challenging for researchers. From that fact, we find flat outputs for the crane system, from which a feedforward controller is built. Then, a model prediction controller based on the candidate Lyapunov function will be combined to set constraints on the states and improve the stability of the controller. Based on the algorithm’s idea, this report’s presentation structure will include covering the contents from the method overview to the controller construction steps, as well as the simulation and testing process on specific objects.