At present, the problems of underactuated, nonlinearity, and poor real-time performance are common in the research of ship motion control methods. The modeling of ship dynamics is one of the key points in ship controller design This paper uses Gaussian process regression in machine learning to identify ship model, in which an empirical ship maneuvering is used to generate state information data sets for regression training, which reduces the computational cost by using only low data volume for training. This approach avoids the calculation of hydrodynamic derivatives in the traditional mechanism modeling process and simplifies to optimize a small number of hyperparameters of the kernel function in Gaussian regression. Finally, the accuracy and robustness of the regression model are tested by cross-validation. Simulation results show that Gaussian process regression can be accurately used to identify nonparametric dynamic systems of ships.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dynamic system identification of underactuated ship dynamics based on Gaussian process regression


    Contributors:
    Zhang, Pei (author) / Liu, Jialun (author) / Xie, Lingli (author) / Li, Shijie (author)


    Publication date :

    2021-10-22


    Size :

    4446629 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Dynamics Analysis and Tracking Control of Underactuated Offshore Ship-To-Ship Crane System

    Luan, Guangrui / Zhang, Menghua / Ning, Donghong | Springer Verlag | 2025


    Ship control motion forecasting method and system based on local Gaussian process regression

    OUYANG ZILU / ZOU LU / LIU JINZHOU et al. | European Patent Office | 2023

    Free access

    Extraction of Ship Route Using Gaussian Process Regression for Passenger Ships

    Liu, Zhao / Gong, Zhenyu / Zhang, Mingyang et al. | IEEE | 2023


    Identification of Hydrodynamic Derivative and Motion Modeling of Underactuated Ship Based on CFD

    Yuan, Shouzheng / Liu, Zhilin / Zheng, Linhe | British Library Conference Proceedings | 2019