Neural network based fin control for ship roll stabilization with guaranteed robustness

To reduce the ship roll motion, an adaptive robust fin controller based on a feedforward neural network is proposed. The dynamics of the fin actuator is considered in the plant of a roll-fin cascaded system with uncertainties which refer to as the modeling errors and the environmental disturbance induced by waves. An on-line feedforward neural network is constructed to account for the uncertainties. Lyapunov design is employed to obtain the fin stabilizer with guaranteed robustness. Simulation results demonstrate the validity of the controller designed and the superior performance over a conventional PD controller.