## 1 Introduction  
With the advances in the shipping industry, improved ship maneuverability and propulsion efficiency are required. The controllable pitch propeller is widely applied in ship thrust systems due to its good properties. The control research associated with this propeller is fundamental for enhancing the navigational safety and economic performance of ships. Extensive research has been conducted on the controller algorithm. Traditional methods, such as the proportional-integral-derivative (PID) control and fuzzy control, have been widely applied to control controllable pitch propellers [1, 2]. By optimizing the control parameters and structures, researchers have upgraded the stability and response speed of control systems. For instance, the PID accurately controls the propeller speed and pitch angle by adjusting the proportional, integral, and derivative coefficients. Thanks to artificial intelligence technology advancements, intelligent control algorithms (i.e., neural networks and genetic algorithms) have been introduced into controllable pitch propellers. Scholarly efforts have been made to address the uncertainty when evaluating the reliability and availability of controllable pitch propeller hydraulic systems. For instance, [PERSON] [3] applied D-S evidence theory and dynamic Bayesian networks to establish a new method for assessing their reliability and availability. [PERSON] [4] predicted the system's reliability using the failure rate prediction method and obtained each unit's failure rate and reliability curve.  
[PERSON] [5] designed an adjustable pitch propeller control hydraulic system comprising an electro-hydraulic directional control valve and a proportional directional control valve, and the corresponding control strategies were proposed. [PERSON] [6] employed the fuzzy method to control a controllable pitch propeller. However, the accuracy of this method is difficult to guarantee. [PERSON] [7] reported that the gain loop could be treated as a nonlinear function within a specific error range; moreover, pertinent research on the ship pitch control propeller with nonlinear PID was conducted, further improving the accuracy and efficiency. [PERSON] [8] constructed a joint controller with load protection for adjustable pitch propellers to achieve maximum thrust effectiveness. [PERSON] [9] conducted simulation research on the ship's controllable pitch propeller control system based on the PID. For the controllable pitch propeller control system, existing control algorithms still have limitations regarding timeliness. At the same time, the traditional design process for marine control systems faces practical issues such as long development cycles, complex procedures, and low development efficiency, which have made it increasingly difficult to meet development demands. In fields like automotive electronics and aerospace, the model-based design (MBD) method has been proven to be an effective controller design approach. Transplanting this method into the design and development of controllable pitch propeller control system undoubtedly represents an effective approach. The model predictive control (MPC) algorithm allows efficient constraint control and easy solutions to the optimal control problems arising from its limitations [10; 11]. This algorithm is iterative and online in optimizing the control. It has the characteristics [12; 13; 14] of a control structure based on model prediction, rolling optimization, and feed-forward feedback, enabling the fast and accurate responses of the controller. Based on the above analysis, the MPC can design the controller within the controllable pitch propeller control system.  
The structure of this paper is as follows. Section 2 presents the simulation model of the controllable pitch propeller. In Section 3, the MPC design is introduced. Section 4 outlines the semi-physical simulation experiment and experimental tests. The concluding remarks are presented in Section 5.