For various specialized maritime operations, predicting the future motion responses of structures is essential. For example, ship-borne helicopter landings require a predictable time frame of 6 to 8 s, while avoiding risks during ship navigation in waves calls for a 15-s prediction window. In this work, a real-time prediction method of future ship motions using the Long Short-Term Memory Neural Network (LSTM) is introduced. A direct multi-step output approach is used to continually update with the most recent data for prediction. This method can model the nonlinear time series of ship motions leveraging LSTM’s capabilities, and User Datagram Protocol (UDP) is used between devices to achieve low-latency data transfer. The performance of this framework is demonstrated and validated through multi-degree-of-freedom motion simulations of a 10000-TEU container ship model in random waves. The results show that all the values of R2 in the four cases are greater than 0.7, and the maximum and minimum values of R2 correspond to predictable time scales of 6 s in Case I and 10 s in Case IV, respectively. This indicates that combining LSTM neural networks with the UDP protocol allows for accurate and efficient predictions and data transmission, and the calculating accuracy of the method decreases as the predictable time scale increases.
Yu et al. (Sun,) studied this question.