Research on Data-Driven Linear Prediction and Real-Time Control Method for Ship Rolling Control System in Beam Sea

Abstract

Predicting a ship’s motion trend in waves is crucial for safe navigation and operation. Existing prediction models are mostly based on the assumption of local linear dynamics, which can achieve great performance in idealized ocean environments. However, ships typically sail in real marine environments with regular or irregular waves, which makes the robustness and real-time performance of ship motion estimation models particularly important. To address this limitation, this paper proposes a global linear predictor (GLP) based on the Koopman operator, which can effectively represent the nonlinear rolling dynamics of ships. Furthermore, the GLP model is used to predict and control the rolling motion of a ship in real time. The proposed method is validated in both regular and irregular wave environments. The simulation experiment results show that the accuracy of the proposed method is about 14% higher than that of other classical methods on ships’ rolling dynamics. And it achieves a more than 91% rolling reduction efficiency in all wave conditions, significantly decreasing the amplitude of a ship’s rolling.

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