Aiming to address the problem of model inaccuracy caused by ship dynamic changes during actual navigation, this study proposes an adaptive online modeling method for ship maneuvering motion based on an error monitoring mechanism.

The method determines model update timing through a model prediction error monitoring mechanism and realizes the adaptive retraining update of the model based on voyage data by combining the sliding window technique and support vector machine. Taking a KCS container ship as the research object, the method is tested and validated under zigzag maneuvering and turning circle motion scenarios with variable speed, and the influence of the error monitoring mechanism’s hyperparameter selection on the online modeling is analyzed.

The simulation results show that the error detection mechanism can effectively reduce the frequency of online model updating and save computational resources. Compared with the offline method, this method can update the model in time when the dynamic characteristics of the ship change, thereby guaranteeing prediction accuracy.

The proposed method is applicable to scenarios in which the dynamic characteristics of ships change due to their own attributes, environmental changes, etc. Thus, it has practical engineering significance by providing a technical method for the online modeling and prediction of ship motion.