Adaptive Neural Network based Sliding Mode Control for Fin Roll Stabilization of Vessels

The paper describes an adaptive neural network based sliding mode control for fin roll stabilization of ocean motoryachts. Radial basis function neural networks are used to adaptively learn system uncertainty bounds and network outputs are used to adjust the gain of the sliding mode control. The analysis of the control stability is based on the Lyapunov theory. The proposed controller is tested by a simulation study based on a nonlinear model, describing the dynamics of a vessel in four degrees of freedom. On this manoeuvring model output sea state disturbances are simulated as multisine time series.