Parameters identification for ship motion model based on particle swarm optimization
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.
ships motion modelling cybernetics programming and algorithm theory