Data-driven simultaneous identification of the 6DOF dynamic model and wave load for a ship in waves

In marine operations, the performance of model-based automatic control design and decision support systems highly relies on the accuracy of the representative mathematical models. Model fidelity can be crucial for safe voyages and offshore operations. This paper proposes a data -driven parametric model identification of a ship with 6 degrees of freedom (6DOF) exposed to waves using sparse regression according to the vessel motion measurements. The features of the complex ship dynamics are extracted and expressed as a linear combination of several functions. Thruster inputs and environmental loads are considered. The hydrodynamic coefficients and wave-induced loads are simultaneously estimated. Unlike earlier studies using a limited number of unknown functions, a library of abundant candidate functions is applied to fully consider the coupling effects among all DOFs. The benefit of the proposed method is that it does not require the exact construction of the library functions. Based on the estimated model, short-term motion prediction is achievable. The algorithm is verified through experiments. The method can be extended to other types of floating structures.

Automated summary

This paper proposes a data-driven parametric model identification method for modeling ships exposed to waves. It aims to improve the performance of model-based automatic control design and decision support systems in marine operations. The method can simultaneously estimate hydrodynamic coefficients and wave-induced loads, enabling short-term motion prediction.