Extreme Short-Term Prediction of Unmanned Surface Vessel Nonlinear Motion Under Waves

Under complex hydrodynamic conditions, Unmanned Surface Vessel (USV) exhibits non-stationary and nonlinear dynamic behaviors. Extreme short-term prediction of such nonlinear motion is therefore critical for ensuring navigational safety. To improve the prediction accuracy, a VMD-CNN-LSTM combined prediction model was applied based on Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-term Memory (LSTM) neural network. The methodology employs VMD to decompose the nonlinear motion time series data of the USV obtained by numerical simulation into stationary Intrinsic Mode Functions (IMFs), subsequently extracting spatial features from these IMFs using CNN layers, and, finally, predicts temporal sequence via the LSTM module. Comparative analyses highlight the better performance of the VMD-CNN-LSTM model over standalone LSTM and CNN-LSTM models in predicting nonlinear motion under varying significant wave heights. At a Prediction Advance Time (PAT) of 3.7 s, the VMD-CNN-LSTM model improves prediction accuracy by 13.3% for a wave height of 1.015 m (Case I) and 54.9% for a wave height of 1.998 m (Case II) compared to the CNN-LSTM model. With a PAT of 5.6 s, the accuracy gains increase to 32.9% for Case I and 94.6% for Case II, demonstrating the model’s robustness in extended prediction scenarios.