In order to obtain a simplified mathematical model of ship motion for intelligent control, this paper takes a Mariner-class vessel as the research object and proposes a sensitivity analysis method combining the standard maneuverability test and PID (proportion-integral-differential) heading control test.

Compound analysis of the control index, maneuverability index and squared loss of typical motion state variables throughout the entire process is performed to obtain a dataset containing multi-dimensional sensitivity coefficients. A K-means machine learning algorithm is introduced to perform cluster analysis on the dataset. The sensitivity division of hydrodynamic derivatives is completed and the model is simplified.

Contrastive simulation tests of heading control and track control are carried out among the simplified model, former simplified model and complete model, and the results show that the sensitivity analysis method proposed in this paper is effective and the model proposed in this paper has higher control prediction accuracy.

The method proposed in this paper has certain significance for guiding ship motion modeling for intelligent control.