Comparative Evaluation of Rudder Excitation Signals for Bayesian Identification of an MMG Model

Accurate maneuvering models are crucial for autonomous ships. Traditional methods such as model tests and CFD analysis require considerable time and cost to construct white-box models, while black-box models lack interpretability. Although system identification using operational data to identify white-box model parameters is efficient, standard maneuvers often lack sufficient excitation signals to yield generalizable models, and random maneuvers are impractical on full-scale ships. This study comparatively examines practical excitation signals that are easily implementable on full-scale ships to obtain informative data. Using pseudo-observation data of a KVLCC2 model ship, hydrodynamic coefficients of the MMG model are identified via a Markov Chain Monte Carlo (MCMC) method to quantify parameter uncertainty while explicitly accounting for observation noise. Among the tested cases, the linearly decreasing rudder-angle signal yielded the most balanced predictive performance for the turning and zig-zag maneuvers. In this numerical case study, the signal covered the full allowable rudder-angle range and generated motions ranging from turning to approximately straight-line behavior. The identified models also retained similar predictive accuracy under a specific synthetic perturbation introduced between the commanded and actual rudder angles. These results indicate that a linearly decreasing rudder-angle signal is a promising candidate for acquiring informative data for MMG model identification.