Nonparametric Modeling and Control of Ship Steering Motion Based on Local Gaussian Process Regression

This paper aims to study the nonparametric modeling and control of ship steering motion. Firstly, the black box response model is derived based on the Nomoto model. Then, the establishment of a nonparametric response model and prediction of ship steering motion are realized by applying the local Gaussian process regression (LGPR) algorithm. To assess the performance of LGPR, two cases are studied, including a Mariner class vessel by using simulation data and a KVLCC2 tanker model by using experimental data. The results reveal that the response model identified by LGPR presents good prediction accuracy and low computational burden. Finally, the identified response model is used as the basis for developing the ship heading controller, and the results demonstrate that the proposed controller is able to achieve good dynamic performance.

Automated summary

This paper investigates the nonparametric modeling and control of ship steering motion. A black box response model is derived based on the Nomoto model. The local Gaussian process regression (LGPR) algorithm is applied to establish a nonparametric response model and predict the ship steering motion. The performance of LGPR is assessed using simulation and experimental data. The results show that the identified response model by LGPR has good prediction accuracy and low computational burden. A ship heading controller is developed based on the identified response model, which demonstrates good dynamic performance.