Ship motion stabilizing control using a combination of model predictive control and an adaptive input disturbance predictor

Abstract

When ships travel on the high seas, changes in the sea states and the sailing conditions induce significant uncertainties in the hydrodynamics, leading to 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 an auto-regressive model of the input disturbance and then compensating for the predicted disturbance in the MPC framework. MPC is more appropriate than classical control when dealing with constraints. The adaptive disturbance model enhances not only the ship adaptability when travelling in varying sea conditions but also the robustness of the control system with model uncertainties. This combines the advantages of MPC and the adaptive model, and avoids performance degradations caused by estimation errors of the state observer which is commonly used within the MPC framework to estimate output disturbances. A numerical simulation shows that this combination works very well.

Keywords

model predictive control ship motion stabilizing input disturbance auto-regression

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