Research on optimal pitch control of CPP based on RBF-ADRC

The controllable pitch propeller (CPP) pitch control system is a nonlinear and uncertain system. Due to the strong coupling between the main engine, propulsion shafting, and CPP, as well as the constraints on the CPP's execution capability from disturbances such as wind, waves, and currents, the CPP pitch control system requires high maneuverability and precision. This paper proposes a pitch control algorithm for CPP based on the Radial Basis Function (RBF) neural network and the Active Disturbance Rejection Controller (ADRC), designs an RBF-ADRC-CPP pitch control system, establishes its MATLAB /Simulink model, and uses the RBF neural network to optimize the ADRC controller. The anti-interference ability, control accuracy, and response speed of the system are observed through simulation. The simulation results show that the ADRC system has better control accuracy and anti-interference ability compared to the PID system. At the same time, the RBF-ADRC-CPP system optimized by the RBF neural network has a better response speed compared to the unoptimized ADRC system, proving that the three performance indicators of the RBF-ADRC-CPP system are significantly superior to the PID system.