Article(id=1240655020287390585, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.04.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1727712000000, receivedDateStr=2024-10-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724807092, onlineDateStr=2026-03-17, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724807092, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724807092, creator=13701087609, updateTime=1773724807092, updator=13701087609, issue=Issue{id=1240655017040991028, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='4', pageStart='1', pageEnd='199', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724806318, creator=13701087609, updateTime=1773725219619, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656750618145316, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656750618145317, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=36, endPage=46, ext={EN=ArticleExt(id=1240655022309045246, articleId=1240655020287390585, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Identification modeling based on deep convolutional neural network for AUV 6-DOF motion, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=

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 (BiLSTM) 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-LSTM-Attention 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 (R²), 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.

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针对自主水下航行器（AUV）的动力学建模问题，考虑AUV六自由度运动的非线性，提出一种基于深度卷积神经网络的非线性系统黑箱辨识建模方法。提取舵信号最大幅值对应频率，作为变分模态分解（VMD）降噪方法的设置阈值，降低AUV模型试验数据噪声，解决了VMD分层参数难以调谐的问题；利用双向长短期记忆（BiLSTM）和注意力机制，建立非线性系统黑箱模型，利用Adam优化方法求解AUV六自由度运动黑箱模型；采用AUV船模试验数据开展模型训练和预测验证，并与CNN-LSTM、CNN-BiLSTM和CNN-LSTM-Attention建模方法作比较，求解并分析AUV运动的速度项、姿态角和运动轨迹。试验结果表明：深度卷积神经网络的均方根误差（RMSE）、决定系数（R²）和平均绝对百分比误差（SMAPE）比CNN-LSTM模型分别改善了79.29%、3.84%和74.41%，验证运动建模方法的可行性和有效性。该方法能为水下航行器的精确避碰和自主导航提供一种有效的动力学建模策略。

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李辰宇（1999—），男，硕士生，研究方向为船舶运动建模。E-mail: lichenyu251@mails.ucas.an.cn

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李辰宇（1999—），男，硕士生，研究方向为船舶运动建模。E-mail: lichenyu251@mails.ucas.an.cn

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