REFERENCES

1. Banerjee I, Pangule RC, Kane RS. Antifouling coatings: recent developments in the design of surfaces that prevent fouling by proteins, bacteria, and marine organisms. Adv Mater 2011;23:690-718.

2. Chen M, Zhu D, Pang W, Chen Q. An effective strategy for distributed unmanned underwater vehicles to encircle and capture intelligent targets. IEEE T Ind Electron 2024;71:12570-80.

3. Xu F, Zou ZJ, Yin JC, Cao J. Identification modeling of underwater vehicles’ nonlinear dynamics based on support vector machines. Ocean Eng 2013;67:68-76.

4. Luque JCC, Donha DC, de Barros EA. AUV parameter identification. IFAC Proc Vol 2009;42:72-7.

5. Huajun Z, Xinchi T, Hang G, Shou X. The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification algorithm. Int J Adv Robot Syst 2020;17:1729881420921016.

6. Ahmed F, Xiang X, Jiang C, Xiang G, Yang S. Survey on traditional and AI based estimation techniques for hydrodynamic coefficients of autonomous underwater vehicle. Ocean Eng 2023;268:113300.

7. Wang D, Wan J, Shen Y, Qin P, He B. Hyperparameter optimization for the LSTM method of AUV model identification based on Q-learning. J Mar Sci Eng 2022;10:1002.

8. van de Ven PWJ, Johansen TA, Sørensen AJ, Flanagan C, Toal D. Neural network augmented identification of underwater vehicle models. IFAC Proc Vol 2004;37:263-8.

9. Chocron O, Vega EP, Benbouzid M. Dynamic reconfiguration of autonomous underwater vehicles propulsion system using genetic optimization. Ocean Eng 2018;156:564-79.

10. Cardenas P, de Barros EA. Estimation of AUV hydrodynamic coefficients using analytical and system identification approaches. IEEE J Oceanic Eng 2020;45:1157-76.

11. Lin M, Yang C, Li D. Hybrid strategy based model parameter estimation of irregular-shaped underwater vehicles for predicting velocity. Robot Auton Syst 2020;127:103480.

12. Prawin J, Rao ARM, Lakshmi K. Nonlinear parametric identification strategy combining reverse path and hybrid dynamic quantum particle swarm optimization. Nonlinear Dynam 2016;84:797-815.

13. Sun B, Pang W, Chen M, Zhu D. Development and experimental verification of search and rescue ROV. Intell Robot 2022;2:355-70.

14. Zhou G, Xiang X, Liu C. Parameter identification and model prediction path following control of underactuated AUV: methodology and experimental verification. Control Eng Pract 2023;141:105729.

15. Hong L, Fang R, Cai X, Wang X. Numerical investigation on hydrodynamic performance of a portable AUV. J Mar Sci Eng 2021;9:812.

16. Bentes C. Modeling of an autonomous underwater vehicle. 2016. Available from: https://api.semanticscholar.org/CorpusID:198363077. [Last accessed on 25 Jun 2024].

17. Wang C, Zhang F, Schaefer D. Dynamic modeling of an autonomous underwater vehicle. J Mar Sci Technol 2015;20:199-212.

18. Bao H, Zhu H. Modeling and trajectory tracking model predictive control novel method of AUV based on CFD data. Sensors 2022;22:4234.

19. Li J, Wang Y, IV T, Di F. Model identification method of intervention AUV system based on numerical simulation. In: Second International Conference on Mechanical Design and Simulation (MDS 2022); Wuhan, China. pp. 953-69.

20. Fossen TI. Handbook of marine craft hydrodynamics and motion control. 2011.

21. Luo YH, Wu JM, Zhou HF. Trajectory tracking control of underwater vehicle based on hydrodynamic parameters calculated by CFD. Chin J Ship Res 2022;17:237-45,272.

22. Shi W, Song S, Wu C, Chen CLP. Multi pseudo Q-learning-based deterministic policy gradient for tracking control of autonomous underwater vehicles. IEEE Trans Neural Netw Learn Syst 2019;30:3534-46.

Intelligence & Robotics
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