Identification modeling based on DBO-Deep-TCN for AUV 6-DOF motion

LI Chenyu, MEI Bin, ZHANG Jie, LIU Chen

doi:10.16411/j.cnki.issn1006-7736.2025.01.004

Journal of Dalian Maritime University >

2025 , Vol. 51 >Issue 1: 31 - 42

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2025.01.004

Identification modeling based on DBO-Deep-TCN for AUV 6-DOF motion

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(Navigation College, Dalian Maritime University, Dalian 116026, China)

Online published: 2024-09-28

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Abstract

Aiming at the nonlinear and strong coupling characteristics of AUV 6-DOF motion, a Deep bidirectional Temporal Convolutional Networks based on dung beetle optimization (DBO) is proposed in this paper as a nonlinear system identification modeling method. First, a bidirectional Temporal Convolutional Networks (BiTCN), a bidirectional Gated Recurrent Unit (BiGRU), and an Attention mechanism (self-attention) are used to construct a Deep bidirectional Temporal Convolutional Networks (Deep-BiTCN) to establish a 6-DOF nonlinear black-box model of the AUV. Secondly, in order to improve the accuracy of Deep-TCN model prediction, this paper uses DBO algorithm to optimize the model hyper-parameters. Finally, the validity and feasibility of the motion modeling method in this paper are verified by comparing with support vector machine (SVM) and random forest (RF) model. The experimental results show that, compared with Deep-BiTCN, the root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of the DBO-Deep-BiTCN algorithm model are reduced by 58.94% and 49.22%, respectively, and the coefficient of determination (R2) is improved by 0.73%; the AUV 6-DOF motion model based on DBO-Deep-BiTCN has high accuracy and strong convergence, and avoids the motion nonlinearity leads to the problems of large forecast error and easy dispersion of the motion system under strong coupling, which can provide an effective strategy for AUV 6-DOF motion identification.

Key words： temporal convolutional networks; DBO; attention mechanism; AUV model test

Cite this article

LI Chenyu, MEI Bin, ZHANG Jie, LIU Chen . Identification modeling based on DBO-Deep-TCN for AUV 6-DOF motion[J]. Journal of Dalian Maritime University, 2025 , 51(1) : 31 -42 . DOI: 10.16411/j.cnki.issn1006-7736.2025.01.004

References

[1]宋保维,潘光,张立川,等.自主水下航行器发展趋势及关键技术[J].中国舰船研究,2022,17(5):27-44.

SONG B W, PAN G, ZHANG L C, et al. Development trend and key technologies of autonomous underwater vehicles[J]. Chinese Journal of Ship Research, 2022,17(5):27–44. (in Chinese)

[2]FOSSEN T I. Handbook of marine craft hydrodynamics and motion control[J]. Sussex/John Willy & Sons Ltd, 2011.

[3]VIGNESH D, ASOKAN T, VIJAYAKUMAR R. Performance analysis of a caudal fin in open water and its coupled interaction with a biomimetic AUV[J]. Ocean Engineering, 2024, 291:116348.

[4]张秀凤,王晓雪,孟耀,等.船舶运动建模与仿真研究进展及未来发展趋势[J].大连海事大学学报, 2021, 47(1):1-8.

ZHANG X F, WANG X X, MENG Y, et al. Research progress and future development trend of ship motion modeling and simulation[J]. Journal of Dalian Maritime University, 2021,47(1):1-8. (in Chinese)

[5]MARTIN J E, HAMMOND M, ROBER N, et al. Reduced order model of a generic submarine for maneuvering near the surface[J]. arXiv preprint arXiv: 2212.09821, 2022.

[6]ALEXANDERSSON M, MAO W, RINGSBERG J W, et al. System identification of a physics-informed ship model for better predictions in wind conditions[J]. Ocean Engineering, 2024, 310:118613.

[7]RAMIREZ W A, KOCIJAN J, LEONG Z Q, et al. Dynamic system identification of underwater vehicles using multi-output Gaussian processes[J]. International Journal of Automation and Computing, 2021,18(5):681-693.

[8]Eng Y H, Teo K M, Chitre M, et al. Online system identification of an autonomous underwater vehicle via in-field experiments[J]. IEEE Journal of Oceanic Engineering, 2015, 41(1): 5-17.

[9]GIBSON S B, STILWELL D J. Hydrodynamic parameter estimation for autonomous underwater vehicles[J]. IEEE Journal of Oceanic Engineering, 2018,45(2):385-394.

[10]CARDENAS P, DE BARROS E A. Estimation of AUV hydrodynamic coefficients using analytical and system identification approaches[J]. IEEE Journal of Oceanic Engineering, 2019, 45(4): 1157-1176.

[11]RAMIREZ W A. Gaussian processes applied to system identification, navigation and control of underwater vehicles[D]. Australia: University of Tasmania, 2019.

[12]HOYER M. System Identification and Machine Learning[D]. Norway: NTNU, 2021.

[13]BADI MIRZAI. Physics-Informed Deep Learning for System Identification of Autonomous Underwater Vehicles: A Lagrangian Neural Network Approach[J]. 2021.

[14]SILVER D, HUANG A, MADDISON C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature. 2016,529(7587):484-9.

[15]张振,崔春义,张鹏,等.基于一维卷积GRU网络的导管架平台动力响应实时预测与分析[J].大连海事大学学报,2023,49(2):23-32.

ZHANG Z, CUI C Y, ZHANG P, et al. Real-time prediction and analysis of jacket platform dynamic response based on one-dimensional convolution and GRU network[J]. Journal of Dalian Maritime University, 2023,49(2):23-32. (in Chinese)

[16]DONG L, WANG H D, LOU J K. An attention mechanism model based on positional encoding for the prediction of ship maneuvering motion in real sea state[J]. Journal of Marine Science and Technology, 2024,29(1):136-152.

[17]WANG N, KONG X, REN B, et al. SeaBil: Self-attention-weighted ultrashort-term deep learning prediction of ship maneuvering motion[J]. Ocean Engineering. 2023,287:115890.

[18]DONG L, WANG H D, LOU J K. An attention mechanism model based on positional encoding for the prediction of ship maneuvering motion in real sea state[J]. Journal of Marine Science and Technology, 2024,29(1):136-152.

[19]SAGATUN S I, FOSSEN T I. Lagrangian formulation of underwater vehicles' dynamics. InConference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics[C]// IEEE,1991:1029-1034.

[20] ZHOU T, YANG X, REN H X, et al. The prediction of ship motion attitude in seaway based on BSO-VMD-GRU combination model[J]. Ocean Engineering, 2023,288:115977.

[21]ZHANG B, WANG S, DENG L, et al. Ship motion attitude prediction model based on IWOA-TCN-Attention[J]. Ocean Engineering. 2023,272:113911.

[22]白伟伟.基于局部加权学习的船舶操纵运动辨识建模[D].大连:大连海事大学,2018.

BAI W W. Ship maneuvering motion identification modeling based on local weighted learning[D]. Dalian：Dalian Maritime University, 2018. (in Chinese)

[23]张钊.基于非参数方法的船舶操纵运动辨识建模[D].大连:大连海事大学,2024.

ZHANG J. Non-parametric methods for ship maneuvering motion identification modeling[D]. Dalian：Dalian Maritime University, 2024. (in Chinese)

[24]XUE J, SHEN B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing. 2023,79(7):7305-7336.

[25]FU J, WU C, WANG J, et al. Lithium-ion battery SOH prediction based on VMD-PE and improved DBO optimized temporal convolutional network model[J]. Journal of Energy Storage. 2024,87:111392.

[26]潘志远,卜凡亮.基于蜣螂算法优化的DV-Hop定位算法[J].电子测量与仪器学报,2023,37(7):33-41.

PAN Z Y, BU F L. DV-Hop positioning algorithm based on dung beetle algorithm optimization[J]. Journal of Electronic Measurement and Instrumentation, 2023, 37(7): 33-41. (in Chinese)

[27] OVERPELT, B, NIENHUIS, B, ANDERSN B. Free running manoeuvring model tests on a modern generic SSK class submarine (BB2). In:Pacific International Maritime Conference. MARIN, 2015:1-14.

[28] 任俊生.船舶运动与控制[M]. 大连：大连海事大学出版社.2021.

REN J S.Ship Motion and Control[M].Dalian:The Press of Dalian Maritime University ,2021. (in Chinese)

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