Identification of Nonlinear Ship Model Parameters Based on the Turning Circle Test

Abstract

This work presents a contribution to solving the problem of identification of ship model parameters using the experimental results from a particular trial test. The innovation of this paper lies in the fact that for this identification purpose it is necessary to know only the turning radius that describes the ship during the performance of the turning test trial. A relatively complex nonlinear model of Norrbin has been chosen as a basis because it represents the ship's dynamics appropriately, as proven through experimental measurements obtained during the course change test. The proposed algorithm of identification of the four ship model parameters is based on an adaptive procedure and the backstepping theory. Another additional coefficient can be determined by an alternative procedure. The knowledge of the true values that characterize the dynamic of a ship is fundamental in the ship steering control that is carried out by autopilots. The simulation results show the suitability of the proposed procedure.

Navigation vector based ship maneuvering prediction

“…Several studies on predicting ship maneuvers are presented in the recent literature and that can be divided into two steps: state & parameter estimation and trajectory prediction. Various state & parameter estimation approaches in ship navigation are proposed by the following studies with the respective methods: linear continuous time domain model with discrete time measurements [13], Kalman filter [14], extended Kalman Filter and Second Order Filter [15], nonlinear Norrbin model [16], non-linear ship maneuvering mathematical model [17], Support Vector Regression (SVR) [18], and recursive neural networks ( [19] - [20]). Similarly, various trajectory prediction approaches in ship navigation are proposed by the following studies with the respective methods: neural networks [21], Maneuvering Modeling Group (MMG) standard method [22], autoregressive moving average (ARMA) and neural networks [23].…”

Nonparametric Modelling of Ship Dynamics Using Puma Optimizer Algorithm-Optimized Twin Support Vector Regression

“…Muske et al [20] identified a nonlinear dynamic model of an unmanned vessel using parameter estimation data from towing experiments. Haro Casado et al [21] proposed a four-parameter identification algorithm for ship models based on adaptive processes and backstepping theory. This work makes a contribution to solving the problem of parameter identification for ship models based on experimental results.…”