Grey-box identification modeling of ship maneuvering motion based on LS-SVM

Abstract

The establishment of a high precision ship motion model is helpful to predict the motion of ships in a short time, and improve the safety and stability of marine operations. Determining the parameters is the key to ensuring the accuracy of the model. In this paper, a 4-DOF grey-box model of ship maneuvering motion is established based on the MMG model. Combined with the training datasets obtained from the designed tests, the least square support vector machine (LS-SVM) method is used to identify the parameters in the grey-box model. It is verified that the model has good applicability in a clean system by comparing the results. Three different levels of noise are applied to the training datasets to simulate the system measurement errors, and the prediction accuracies of three ship test cases at different levels of disturbance are analyzed by calculating RMSE and MAPE. The results show that the model established by the grey-box identification modeling method has good validity and robustness under disturbance, and can provide a certain degree of reliable support for ship motion prediction.