Ship stabilization control using an adaptive input disturbance predictor

When ships travel on the oceans, changes in the sea states and the sailing conditions will induce significant uncertain hydrodynamics, leading to a deterioration in the performance of traditional stabilization systems. To overcome this problem, a combination of Model Predictive Control (MPC) and an adaptive input disturbance predictor is proposed. This combination predicts the wave disturbance force by using a predictive model of the input disturbance and then compensating for the predicted disturbance within the MPC framework. This has the advantages of MPC and the adaptive model, and avoids the complicated robust tuning of a state observer which is commonly used within the MPC framework to reject output disturbances. Model Predictive Control is better than classical control at dealing with constraints, and the adaptive disturbance model enhances the ship adaptability when traveling in varying sea conditions. Very good predictions of the ship motion are obtained with less degradation under changes of sailing conditions, thus achieving very good closed loop performance in the MPC framework. An adaptive input disturbance predictor based on the time series Auto Regressive (AR) model is used in a numerical simulation which shows that this combination works very well.