Non-parametric dynamic system identification of ships using multi-output Gaussian Processes

Abstract

A novel application of non-parametric system identification algorithm for a surface ship has been employ on this study with the aim of modelling ships dynamics with low quantity of data. The algorithm is based on multi-output Gaussian processes and its ability to model the dynamic system of a ship without losing the relationships between coupled outputs is explored. Data obtained from the simulation of a parametric model of a container ship is used for the training and validation of the multi-output Gaussian processes. The required methodology and metric to implement Gaussian processes for a 4 degrees of freedom (DoF) ship is also presented in this paper. Results show that multi-output Gaussian processes can be accurately applied for non-parametric dynamic system identification in ships with highly coupled DoF.