Parameter Identification of an Unmanned Surface Vehicle Based on Distance-based Multi-Objective Particle Swarm Optimization Algorithm

Abstract

In this paper, a multi-objective particle swarm optimization algorithm based on the relative distance strategy is proposed to realize hydrodynamic coefficient identification, which can improve the accuracy of parameter identification without determining the weight manually. Firstly, single-objective particle swarm optimization (PSO) is used to identify the parameters of the model with a low coupling degree. Then, the parameters of the strongly coupled sway and yaw motion model are regarded as multi-objective, and a set of unbiased Pareto optimal solutions are obtained by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the distance function of the Pareto solution is constructed to determine the parametric solution of the model. Finally, experimental identification is carried out by using the data set collected from the real ship. Experimental results show that the accuracy of model parameters obtained by this identification method is about 91.6%, and there is no need to set the weighting coefficient.