This study aims to develop a PD control system based on the neural network algorithm to reduce the ship rolling motion in the desired track through rudder and fin actions. The mathematical model including sea-keeping and maneuvering characteristics is developed in the present paper and the nonlinear time history of ship motions is solved by the fourth order Runge-Kutta method. In order to achieve the purpose of roll reduction and track keeping, the rudder and fin stabilizer are used as the control tools for the ship advancing in the seaway. In addition, the PD controller based on the self-tuning neural network algorithm is applied to achieve the goals of multi-input and multi-output in the control system. Four different types of control modes on a container ship model are studied and the performances are investigated in different sea states. The results indicate that the PD controller based on the self-tuning neural network algorithm applying to the stabilizer fin control for roll reduction and the rudder control for track keeping in the seaway would be suggested.
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