Parameter identification of ship motion mathematical model based on full-scale trial data

Abstract

Using the full-scale trial data of vessel YUKUN, a parameter identification scheme, Support Vector Regression (SVR) combined with modified grey wolf optimizer (MGWO), is proposed for identifying ship motion model. This study establishes response mathematical model and 4 Degrees of Freedom (DOF) nonlinear whole-ship mathematical model of vessel YUKUN. The full-scale trial data of vessel YUKUN are processed and analyzed. In the study of grey box identification modeling, the rough parameters reference values of ship response mathematical model and nonlinear whole-ship mathematical model are obtained by SVR. Based on these rough reference values, the MGWO algorithm and Firefly Algorithm are used to further optimize them to obtain final parameter identification values. The final parameter identification values obtained by the three algorithms and recursive least squares with forgetting factor are substituted into the mathematical model to obtain the prediction results of ship motion state. The final prediction results of ship motion state show that the MGWO algorithm has strong search and optimization ability. In the case that the reference values obtained by SVR are not accurate, MGWO algorithm can be used to find accurate parameters of ship motion mathematical model in a given range. The proposed scheme provides a reference for the parameter identification of ship motion mathematical model.