Parameters identification for ship motion model based on particle swarm optimization

Abstract

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.

Citation

Chen, Y., Song, Y. and Chen, M. (2010), "Parameters identification for ship motion model based on particle swarm optimization", Kybernetes, Vol. 39 No. 6, pp. 871-880. https://doi.org/10.1108/03684921011046636