Research on Maneuvering Motion Prediction for Intelligent Ships Based on LSTM-Multi-Head Attention Model

In complex marine environments, accurate prediction of maneuvering motion is crucial for the precise control of intelligent ships. This study aims to enhance the predictive capabilities of maneuvering motion for intelligent ships in such environments. We propose a novel maneuvering motion prediction method based on Long Short-Term Memory (LSTM) and Multi-Head Attention Mechanisms (MHAM). To construct a foundational dataset, we integrate Computational Fluid Dynamics (CFD) numerical simulation technology to develop a mathematical model of actual ship maneuvering motions influenced by wind, waves, and currents. We simulate typical operating conditions to acquire relevant data. To emulate real marine environmental noise and data loss phenomena, we introduce Ornstein–Uhlenbeck (OU) noise and random occlusion noise into the data and apply the MaxAbsScaler method for dataset normalization. Subsequently, we develop a black-box model for intelligent ship maneuvering motion prediction based on LSTM networks and Multi-Head Attention Mechanisms. We conduct a comprehensive analysis and discussion of the model structure and hyperparameters, iteratively optimize the model, and compare the optimized model with standalone LSTM and MHAM approaches. Finally, we perform generalization testing on the optimized motion prediction model using test sets for zigzag and turning conditions. The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance.