Identification Modeling of Ship Maneuvering Motion Based on AE-MSVR

The strong coupling between the ship’s sway and yaw motion increases the complexity of identifying hydrodynamic derivatives in mathematical models and reduces accuracy. To solve this problem, this paper proposes an identification method Alpha Evolution Multi-output Support Vector Regression (AE-MSVR) based on MSVR combined with AE. This method approaches the yaw and sway motion equations as a multi-input and multi-output (MIMO) problem, utilizing MSVR for modeling and optimizing hyperparameters with AE. It reduces parameter drift by restructuring the regression model’s input–output. Identification data is obtained via zigzag test simulation. The AE-MSVR method successfully identifies linear and nonlinear hydrodynamic derivatives in the 3 degree of freedom (DOF) Abkowitz model. Using clean simulation data, the results show promising agreement with experimental values from planar motion mechanism (PMM) tests and achieve improved accuracy compared with the standard SVR identification method. To assess robustness, simulated noise is introduced at different levels; maneuvering characteristics are evaluated using turning circle tests. Results demonstrate that AE-MSVR achieves promising accuracy in identifying ship hydrodynamic derivatives and shows encouraging robustness against noise. The method provides potential support for ship motion prediction and maneuverability forecasting.