  Designing an autonomous precise controller for ships requires accurate and
reliable ship models, including the ship dynamic model and actuator model.
However, selecting a suitable model for controller design and determining its
parameters pose a significant challenge, considering factors such as ship
actuation, input constraints, environmental disturbances, and others. This
challenge is further amplified for underactuated ships, as obtaining decoupled
experiment data is not feasible, and the limited data available may not
adequately represent the motion characteristics of ships. To address this
issue, we propose a novel model-based parameter estimation approach, called
MBPE-LOGO, for underactuated ship motion models. This method combines local
optimization and global optimization methods to solve the model identification
problem using a dataset generated from real experiments. The effectiveness of
the identified model is verified through extensive comparisons of different
trajectories and prediction steps.
