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