During the voyage of a ship, the performance of a controllable pitch propeller (CPP) is severely affected by the changing load demand and ever-present disturbance from ocean waves, which will also result in model uncertainty. In order to improve the performance of the CPP system, an online high-performance genetic algorithm (HPGA)-based sliding mode control (SMC) strategy is proposed. Firstly, the model of the CPP system is obtained according to the manufacturer’s instructions. Then, a chattering-free sliding mode controller (CF-SMC) is designed for the CPP system, after which the parameters in the CF-SMC are optimized with the HPGA method. Finally, the optimized CF-SMC is applied to an experimental setup of a prototype CPP system. In order to validate the effectiveness of the proposed method, it is compared with a proportional-integral-derivative (PID) controller, which is typically applied on real CPP-systems, with results indicating the superiority of the proposed method.

中文翻译：

---

基于在线高性能遗传算法的可控变桨螺旋桨滑模控制

在船舶航行期间，可控螺距螺旋桨（CPP）的性能会受到不断变化的负载需求和海浪不断干扰的严重影响，这也将导致模型不确定性。为了提高CPP系统的性能，提出了一种基于在线高性能遗传算法（HPGA）的滑模控制（SMC）策略。首先，根据制造商的说明获得CPP系统的模型。然后，为CPP系统设计了无抖动的滑模控制器（CF-SMC），然后使用HPGA方法对CF-SMC中的参数进行优化。最后，将优化的CF-SMC应用于原型CPP系统的实验装置。为了验证所提出方法的有效性，