Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter

The accurate nonlinear modeling of an unmanned surface vessel (USV) is essential for advanced control and operational performance. This paper combines the locally weighted regression (LWR) algorithm and the extended Kalman filter (EKF) for parameter identification using state data from full-scale vessel experiments. To mitigate the effects of disturbances and abrupt changes in the full-scale vessel data, LWR filtering is applied for data smoothing before parameter identification. The EKF is then used to estimate the unknown parameters in the second-order nonlinear Nomoto model of the USV. These parameters are incorporated into the Nomoto model, and simulations are conducted by inputting the same rudder inputs as in the experimental data. The predicted heading angle and yaw rate are compared with experimental results, showing that the mean absolute error (MAE) for the heading angle is within 10° and the MAE for the yaw rate is within 1.5°/s. Additionally, the coefficient of determination (R2) values for both predictions are above 0.93. The simulation results demonstrate that the combination of LWR filtering and EKF effectively identifies parameters and models the nonlinear response of the USV, achieving high accuracy in the established second-order model.