Purpose - The purpose of this paper is to identify the Nomoto ship model parameters accurately, in order to produce a very close match between the predictions based on the model and the full-scale trials. Design/methodology/approach - Various ship maneuvering mathematical models have been used when describing the ship dynamics behavior. The Nomoto ship model is a class of simplified hydrodynamic derivative type models which are the most widely used, accepted and perhaps well developed. To determine the model parameters accurately, particle swarm optimization (PSO) is chosen as an evolution algorithm in this paper. This arithmetic can guarantee the convergence and global optimization ability, and avoid sinking into a local optimal solution. Findings - The process of PSO for identifying the Nomoto ship model parameters is given. Research limitations/implications - Availability of the full-scale trial data are the main limitations. Practical implications - The ship model parameters provide very useful advice in ship's autopilot process. Originality/value - The paper presents a new parameter identification method for the second-order Nomoto ship model based on PSO.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Parameters identification for ship motion model based on particle swarm optimization


    Contributors:

    Published in:

    Kybernetes, London ; 39 , 6 ; 871-880


    Publication date :

    2010


    Size :

    10 Seiten, 14 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





    Ship energy management scheme based on improved particle swarm optimization algorithm

    YIN Bo / WANG Xihuai / XIAO Jianmei | DOAJ | 2020

    Free access


    Individual Model Identification for Turbofan Engine based on Particle Swarm Optimization

    Qian, Junning / Lu, Feng / Qiu, Xiaojie et al. | British Library Conference Proceedings | 2015


    Optimizing ship energy efficiency: Application of particle swarm optimization algorithm

    Wang, Kai / Yan, Xinping / Yuan, Yupeng et al. | SAGE Publications | 2018