Nonparametric modeling of ship maneuvering motions in calm water and regular waves based on R-LSTM hybrid method

Abstract

It is essential to establish reliable method to predict the ship maneuvering motions in complex navigation environments for navigation efficiency and safety. In this paper, a novel hybrid nonparametric modeling method (R-LSTM) is developed by coupling Bidirectional Long Short-Term Memory (Bi-LSTM) with residual network and attention mechanism. Firstly, the prediction capability of the proposed hybrid method is evaluated in the predictions of zigzag maneuvers in calm water. The model test data of a KVLCC2 tanker model are utilized for modeling and validation, and the optimal parameters are determined by Tree-structured Parzen Estimator algorithm. The results demonstrate that the proposed method has higher prediction accuracy and better generalization ability than the classical LSTM method. On this basis, the online modeling method for predicting the turning motion in regular waves is developed along with sliding time window and time series split cross-validation. The model test data of a KCS container ship and a ONRT tumblehome ship under different wave conditions are used for online modeling and real-time prediction. The results indicate that the ship trajectories with trajectory drift and fluctuation features during the turning motion under the wave impacts are captured well by the developed method with satisfactory confidence intervals.