Identification modeling of ship maneuvering motion based on local Gaussian process regression

Abstract

A fast and accurate nonparametric modeling method based on local Gaussian process regression (LGPR) is proposed for the identification modeling and prediction of ship maneuvering motion. The training dataset collected from the free-running model tests of ship maneuvering is automatically divided into a number of clusters according to the similarity criterion by clustering analysis using k-means algorithm. Utilizing the data in each cluster, the corresponding local nonparametric model is identified. The computational cost of training and prediction based on LGPR is reduced compared to that based on the classic Gaussian process regression (CGPR) using the whole training dataset. Taking the KVLCC2 tanker and an unmanned surface vehicle (USV) as study objects, the nonparametric models are identified based on the experimental data of zigzag maneuvers of the KVLCC2 model and random maneuver of the USV. Using the identified models, the zigzag maneuvers of the KVLCC2 model and the random maneuver of the USV, which are not involved in the training data, are predicted. The results show that LGPR has higher computational efficiency than CGPR with acceptable prediction accuracy.