Identification modeling based on deep convolutional neural network for AUV 6-DOF motion

To address the challenges in dynamic modeling of Autonomous Underwater Vehicle（ AUV）,this paper proposes a black-box identification method for nonlinear systems based on deep convolutional neural networks,taking into account the nonlinear characteristics of the AUV’ s six-degree-of-freedom（ 6-DOF） motion. First,the frequency corresponding to the maximum amplitude of the rudder signal is extracted and used as a threshold for Variational Mode Decomposition（ VMD） denoising. This reduces noise in the experimental data of the AUV model and resolves the issue of difficult parameter tuning in VMD decomposition. Then, a black-box model for the nonlinear system is constructed using Bidirectional Long Short-Term Memory（ Bi LSTM） and Attention mechanisms,with the Adam optimization algorithm employed to solve the AUV 6-DOF motion model. Finally,the AUV model data are used for model training and predictive validation,and the results are compared with modeling methods such as CNN-LSTM,CNN-BiLSTM,and CNN-LSTMAttention to analyze the velocity,Euler angles,and trajectory of AUV motion. Experimental results show that,compared to the CNN-LSTM model, the proposed method improves the Root Mean Square Error（ RMSE）, the coefficient of determination（ R~2）,and the Symmetric Mean Absolute Percentage Error（ SMAPE） by 79. 29%,3. 84%,and 74. 41%,respectively,validating the feasibility and effectiveness of the proposed dynamic modeling approach. This method provides an alternative strategy for precise obstacle avoidance and autonomous navigation of underwater vehicles.