An online identification approach for a nonlinear ship motion model based on a receding horizon

Abstract

The ship motion system is a nonlinear control object, and its system parameters exhibit time-varying characteristics with the ship motion state, which increases the difficulty of control. Therefore, parameter identification has an important significance for the stability of ship motion control. Aiming at the real-time identification problem of the nonlinear and time-varying ship motion system during movement, this paper reconstructs the ship motion system with the propeller speed and rudder angle as control variables and designs an online identification algorithm with the sliding time window method based on the extended Kalman filter algorithm. In addition, to solve the problem of noise in ship motion data collected in real-time, a real-time wavelet filter is developed to perform online preprocessing of the input data of the identification algorithm. The applicability of the method is further demonstrated via a model-scale Korea Research Institute of Ships and Ocean Engineering container ship free-running experiments in a basin.

Ship Trajectory Generator under the Interference of Wind, Current and Waves

“…In (5), , ; and are the damping ratio and oscillating frequency, respectively. By identifying the measured data, different and can be obtained, which can reflect the acceleration performance of ships in different gears [ 20 ]. Deceleration is regarded as the reverse process of acceleration and will not be analyzed in detail.…”

Rudder/fin joint anti-rolling control system based on interference model predictive control and sliding mode observer

“…To eliminate the adverse effect of model perturbation on the output, Wu et al (2021) used the unknown nonlinear function to represent the model uncertainty of the ship and built an adaptive neural network to approximate it. In the study of Zheng et al (2021), Kalman filter was used to identify unknown parameters in ship motion systems online. The function approximation and model identification methods have hysteresis and fail to maintain the system's robustness at all times.…”