Authors

Z. A. Barhoum, S. A. Kolyubin

Abstract

Modeling the dynamics of underwater robots is a complex task due to the presence of both parametric and functional uncertainties. These arise from interactions with a viscous medium, a priori uncertainty, and variability in the system’s dynamic parameters, as well as the complexity and computational cost of first-principles models and the challenges of identification procedures. This paper proposes the use of neural network parameterization of ordinary differential equations based on the port-Hamiltonian formalism to develop accurate and computationally efficient dynamic models of underwater robots. These models can be used for trajectory prediction, integration with onboard sensor data for localization systems, and controller synthesis. The proposed approach captures both the physical structure of the system and the impact of uncertainties, enabling the creation of physically grounded, data-driven representations of complex nonlinear dynamics. Comparative experiments with classical identification and modeling methods using real-world data from an underwater robot demonstrate advantages of the proposed method in prediction accuracy and its robustness over long-horizon prediction.

Citation

BibTeX

@article{Barhoum_2025,
  title={{Physically consistent dynamic modeling of underwater robots for robust long-horizon motion prediction}},
  volume={68},
  ISSN={0021-3454},
  DOI={10.17586/0021-3454-2025-68-11-983-995},
  number={11},
  journal={Journal of Instrument Engineering},
  publisher={ITMO University},
  author={Barhoum, Z. A. and Kolyubin, S. A.},
  year={2025},
  pages={983--995}
}

Download the bib file

References

  • Choi H-T, Yuh J (2016) Underwater Robots. Springer Handbooks 595–62 – 10.1007/978-3-319-32552-1_25
  • Antonelli G, Fossen TI, Yoerger DR (2016) Modeling and Control of Underwater Robots. Springer Handbooks 1285–130 – 10.1007/978-3-319-32552-1_51
  • Zhao Y, Hu Z, Du W, Geng L, Yang Y (2024) Research on Modeling Method of Autonomous Underwater Vehicle Based on a Physics-Informed Neural Network. JMSE 12(5):801. https://doi.org/10.3390/jmse1205080 – 10.3390/jmse12050801
  • Ramirez WA, Kocijan J, Leong ZQ, Nguyen HD, Jayasinghe SG (2021) Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes. Int J Autom Comput 18(5):681–693. https://doi.org/10.1007/s11633-021-1308- – 10.1007/s11633-021-1308-x
  • Wu H-M, Karkoub M (2021) Finite-time robust tracking control of an autonomous underwater vehicle in the presence of uncertainties and external current disturbances. Advances in Mechanical Engineering 13(10). https://doi.org/10.1177/1687814021105342 – 10.1177/16878140211053429
  • Kong F, Guo Y, Lyu W (2020) Dynamics Modeling and Motion Control of an New Unmanned Underwater Vehicle. IEEE Access 8:30119–30126. https://doi.org/10.1109/access.2020.297233 – 10.1109/access.2020.2972336
  • Valeriano-Medina Y, Martínez A, Hernández L, Sahli H, Rodríguez Y, Cañizares JR (2013) Dynamic model for an autonomous underwater vehicle based on experimental data. Mathematical and Computer Modelling of Dynamical Systems 19(2):175–200. https://doi.org/10.1080/13873954.2012.71722 – 10.1080/13873954.2012.717226
  • Measurement of an AUV in a Towing Tank. JAFM 12(3):947–959. https://doi.org/10.29252/jafm.12.03.2952 – 10.29252/jafm.12.03.29525
  • {“status”:”error” – 10.1016/j.oceaneng.2008.08.008
  • Cortez WS, Vasisht S, Tuor A, Koch J, Drgoňa J, Vrabie D (2023) Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles. IFAC-PapersOnLine 56(1):228–233. https://doi.org/10.1016/j.ifacol.2023.02.03 – 10.1016/j.ifacol.2023.02.039
  • Sousa CD, Cortesao R (2019) Inertia Tensor Properties in Robot Dynamics Identification: A Linear Matrix Inequality Approach. IEEE/ASME Trans Mechatron 24(1):406–411. https://doi.org/10.1109/tmech.2019.289117 – 10.1109/tmech.2019.2891177
  • Wang J, Le Vang T, Pyrkin AA, Kolyubin SA, Bobtsov AA (2018) Identification of Piecewise Linear Parameters of Regression Models of Non-Stationary Deterministic Systems. Autom Remote Control 79(12):2159–2168. https://doi.org/10.1134/s000511791812006 – 10.1134/s0005117918120068
  • Muñoz F, Cervantes-Rojas JS, Valdovinos JM, Sandre-Hernández O, Salazar S, Romero H (2021) Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties. Applied Sciences 11(6):2797. https://doi.org/10.3390/app1106279 – 10.3390/app11062797
  • Szymkowiak M (2025) Using recurrent neural networks to build a data-driven model of an autonomous underwater vehicle. Transportation Research Procedia 83:417–424. https://doi.org/10.1016/j.trpro.2025.03.00 – 10.1016/j.trpro.2025.03.008
  • Sun H, Guo W, Lan Y, Wei Z, Gao S, Sun Y, Fu Y (2022) Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression. JMSE 10(5):575. https://doi.org/10.3390/jmse1005057 – 10.3390/jmse10050575
  • Lei L, Gang Y, Jing G (2022) Physics-guided neural network for underwater glider flight modeling. Applied Ocean Research 121:103082. https://doi.org/10.1016/j.apor.2022.10308 – 10.1016/j.apor.2022.103082
  • Duong T, Atanasov N (2021) Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control. Robotics: Science and Systems XVI – 10.15607/rss.2021.xvii.086
  • Duong T, Altawaitan A, Stanley J, Atanasov N (2024) Port-Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics Learning and Control. IEEE Trans Robot 40:3695–3715. https://doi.org/10.1109/tro.2024.342843 – 10.1109/tro.2024.3428433
  • Singh M, Dharmadhikari M, Alexis K (2024) An Online Self-calibrating Refractive Camera Model with Application to Underwater Odometry. 2024 IEEE International Conference on Robotics and Automation (ICRA) 10005–1001 – 10.1109/icra57147.2024.10610643