Wavelet Neural Network-Based Half-Period Predictive Roll-Reduction Control Using a Fin Stabilizer at Zero Speed

Abstract

Among the commonly used ship-stabilizing devices, the fin stabilizer is the most effective. Since the lift force of the conventional fin stabilizer is proportional to the square of the incoming flow velocity, it has a better anti-rolling effect at higher speeds but a poor anti-rolling effect at low speeds and even no effect at zero speed. A combination of modelling analysis, simulation, and a model ship experiment is used in this paper to study the zero-speed roll-reduction control problem of the fin stabilizer. A simulation model of the rolling motion of a polar expedition ship is established. The lift model of the fin stabilizer at zero speed is established using the theory of fluid mechanics. The proportional–integral–differential (PID) controller is selected to control the fin to achieve zero-speed roll reduction. To obtain a better anti-rolling control effect under variable sea conditions, a wavelet neural network (WNN)-based half-period prediction algorithm is adopted to update and adjust PID control parameters in real time. A simulation was carried out, and the effectiveness of the proposed predictive control algorithm is proved. A reduced-scale ship model was established to carry out the water tank experiment, and the results verify the theoretical analysis and simulation. The results also verify the effectiveness of the proposed control strategy.