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Adaptive Anti-noise Least-Squares Algorithm for Parameter Identification of Unmanned Marine Vehicles: Theory, Simulation, and Experiment

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Abstract

In this paper, an adaptive anti-noise least-squares algorithm (ANLS) is proposed for parameter identification of an unmanned marine vehicle in the presence of measurement noise. As a basis, a horizontal-plane second-order nonlinear Nomoto model is established and transformed into a discrete-time model for parameter identification. Then, a noise reduction term is added to the loss function to achieve a trade-off between the anti-noise effect and parameter identification accuracy. Furthermore, the Levenberg–Marquardt algorithm is embedded into the parameter identification algorithm to achieve adaptive coefficient optimization. Finally, the simulation and experimental data are utilized for parameter identification and performance validation. By comparing with the recursive least-squares algorithm and least-squares support vector machine algorithm, the excellent anti-noise and maneuvering prediction abilities of the proposed ANLS algorithm are verified, i.e., up to 84% reduction of the identification error in the simulation and less than \(4^\circ\) of the heading angle prediction error in the experiment.

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References

  1. Wang, N., Gao, Y., Zhang, X.: Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle. IEEE Trans. Neural Netw. Learn. Syst. 32(12), 5456–5467 (2021)

    Article  MathSciNet  Google Scholar 

  2. Yu, C., Liu, C., Lian, L., Xiang, X., Zeng, Z.: ELOS-based path following control for underactuated surface vehicles with actuator dynamics. Ocean Eng. 187, 106139 (2019)

    Article  Google Scholar 

  3. Liu, X., Zhang, M., Wang, Y., Rogers, E.: Design and experimental validation of an adaptive sliding mode observer-based fault-tolerant control for underwater vehicles. IEEE Trans. Control Syst. Technol. 27(6), 2655–2662 (2019)

    Article  Google Scholar 

  4. Li, M., Yue, L., Li, T., Bai, W.: Observer-based adaptive fuzzy event-triggered path following control of marine surface vessel. Int. J. Fuzzy Syst. 23, 2021–2036 (2021)

    Article  Google Scholar 

  5. Rout, R., Cui, R., Han, Z.: Modified line-of-sight guidance law with adaptive neural network control of underactuated marine vehicles with state and input constraints. IEEE Trans. Control Syst. Technol. 28(5), 1902–1914 (2020)

    Article  Google Scholar 

  6. Qu, X., Liang, X., Hou, Y.: Fuzzy state observer-based cooperative path-following control of autonomous underwater vehicles with unknown dynamics and ocean disturbances. Int. J. Fuzzy Syst. 23(6), 1849–1859 (2021)

    Article  Google Scholar 

  7. Wang, N., Gao, Y., Yang, C., Zhang, X.: Reinforcement learning-based finite-time tracking control of an unknown unmanned surface vehicle with input constraints. Neurocomputing 484(1), 26–37 (2021)

    Google Scholar 

  8. Xiang, G., Xiang, X.: 3D trajectory optimization of the slender body freely falling through water using cuckoo search algorithm. Ocean Eng. 235, 109354 (2021)

    Article  Google Scholar 

  9. Prokopyev, I., Sofronova, E.: Study on control methods based on identification of unmanned vehicle model. Proc. Comput. Sci. 186, 21–29 (2021)

    Article  Google Scholar 

  10. Guo, H., Zou, Z.: A RANS-based study of the impact of rudder on the propeller characteristics for a twin-screw ship during maneuvers. Ocean Eng. 239, 109848 (2021)

    Article  Google Scholar 

  11. Wang, N., Han, M., Dong, N., Er, M.J.: Constructive multi-output extreme learning machine with application to large tanker motion dynamics identification. Neurocomputing 128, 59–72 (2014)

    Article  Google Scholar 

  12. Jiang, Y., Wang, X.G., Zou, Z.J., Yang, Z.L.: Identification of coupled response models for ship steering and roll motion using support vector machines. Appl. Ocean Res. 110, 102–607 (2021)

    Article  Google Scholar 

  13. Wang, N., Su, S.: Finite-time unknown observer-based interactive trajectory tracking control of asymmetric underactuated surface vehicles. IEEE Trans. Control Syst. Technol. 29(2), 794–803 (2021)

    Article  Google Scholar 

  14. Wang, N., Ahn, C.: Coordinated trajectory-tracking control of a marine aerial-surface heterogeneous system. IEEE/ASME Trans. Mechatron. 26(6), 3198–3210 (2021)

    Article  Google Scholar 

  15. Ikeda, Y.: Prediction methods of roll damping of ships and their application to determine optimum stabilization devices. Marine Technol. 41, 89–93 (2004)

    Google Scholar 

  16. Seo, J., Kim, D., Ha, J., Rhee, S., Yoon, H., Park, J., Seok, W.C., Rhee, K.: Captive model tests for assessing maneuverability of a damaged surface combatant with initial heel angle. J. Ship Res. 64, 392–406 (2020)

    Article  Google Scholar 

  17. NadalRey, G., McClure DD., Kavanagh, JM., Cassells, B., Cornelissen, S., Fletcher,  DF.,  Gernaey, KV.: Computational fluid dynamics modelling of hydrodynamics, mixing and oxygen transfer in industrial bioreactors with Newtonian broths. Biochem. Eng. J. 177, 108265 (2022)

    Google Scholar 

  18. Costa, A.C., Xu, H., Carlos, G.S.: Robust parameter estimation of an empirical Manoeuvring model using free-running model tests. J. Marine Sci. Eng. 9(11), 1302 (2021)

    Article  Google Scholar 

  19. Abrougui, H., Nejim, S., Hachicha, S., Zaoui, C., Dallagi, H.: Modeling, parameter identification, guidance and control of an unmanned surface vehicle with experimental results. Ocean Eng. 241, 110038 (2021)

    Article  Google Scholar 

  20. Moreno-Salinas, D., Moreno, R., Pereira, A., Aranda, J., de la Cruz, J.M.: Modelling of a surface marine vehicle with kernel ridge regression confidence machine. Appl. Soft Comput. 76, 237–250 (2019)

    Article  Google Scholar 

  21. Ikeda, Y.: A simple prediction formula of roll damping of conventional cargo ships on the basis of Ikeda’s method and its limitation. Proc. 10th Int. Conf. Stab. Ships Ocean Veh. 97, 465–486 (2009)

    Google Scholar 

  22. Xia, L., Zou, Z., Wang, Z., Zou, L., Gao, H.: Surrogate model based uncertainty quantification of CFD simulations of the viscous flow around a ship advancing in shallow water. Ocean Eng. 234, 109206 (2021)

    Article  Google Scholar 

  23. Deng, F., Levi, C., Yin, H., Duan, M.: Identification of an autonomous underwater vehicle hydrodynamic model using three Kalman filters. Ocean Eng. 229, 108962 (2021)

    Article  Google Scholar 

  24. Dong, Z., Xin, Y., Mao, Z., Lifei, S., Yunsheng, M.: Parameter identification of unmanned marine vehicle Manoeuvring model based on extended Kalman filter and support vector machine. Int. J. Adv. Robot. Syst. 16(1), 1–10 (2019)

    Article  Google Scholar 

  25. Xu, P., Cheng, C., Cheng, H., Shen, Y., Ding, Y.: Identification-based 3 DOF model of unmanned surface vehicle using support vector machines enhanced by cuckoo search algorithm. Ocean Eng. 197, 106898 (2020)

    Article  Google Scholar 

  26. Xu, H., Hassani, V., Guedes Soares, C.: Truncated least square support vector machine for parameter estimation of a nonlinear Manoeuvring model based on PMM tests. Appl. Ocean Res. 97, 102076 (2020)

    Article  Google Scholar 

  27. Zhao, J., Zhang, J.A., Zhang, H., Li, Q.: Generalized correntropy induced metric based total least squares for sparse system identification. Neurocomputing 467, 66–72 (2022)

    Article  Google Scholar 

  28. Zhang, X., Ji, J., Xu, J.: Parameter identification of time-delayed nonlinear systems: an integrated method with adaptive noise correction. J. Franklin Inst. 356(11), 5858–5880 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  29. Shariati, H., Moosavi, H., Danesh, M.: Application of particle filter combined with extended Kalman filter in model identification of an autonomous underwater vehicle based on experimental data. Appl. Ocean Res. 82, 32–40 (2019)

    Article  Google Scholar 

  30. Wang, Z., Xu, H., Xia, L., Zou, Z., Soares, C.G.: Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion. Ocean Eng. 216, 107994 (2020)

    Article  Google Scholar 

  31. Hafezi, Z., Arefi, M.M.: Recursive generalized extended least squares and RML algorithms for identification of bilinear systems with ARMA noise. ISA Trans. 88, 50–61 (2019)

    Article  Google Scholar 

  32. Jafari, M., Salimifard, M., Dehghani, M.: Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm. ISA Trans. 53(4), 1243–1252 (2014)

    Article  Google Scholar 

  33. Zhang, G., Zhang, X., Pang, H.: Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scal trial data. ISA Trans. 58, 186–195 (2015)

    Article  Google Scholar 

  34. Zhang, H., Tong, X., Guo, H., Xia, S.: The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification algorithm. Int. J. Adv. Robot. Syst. 17(2), 1–11 (2020)

    Google Scholar 

  35. Zhong, Y., Yu, C., Liu, C., Pei, T., Wang, R., Lian, L.: Recursive parameter identification for second-order K-T equations of marine robot in horizontal motion. Indian J. Geo Marine Sci. 50(11), 890–896 (2021)

    Google Scholar 

  36. Fossen, T.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley, New York (2011)

    Book  Google Scholar 

  37. Chu, S., Mao, Y., Dong, Z., Yang, X.: Parameter identification of high-speed USV maneuvering response model based on maximum likelihood algorithm. Acta Armamentarii 41(1), 127–134 (2020)

    Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 51909161, in part by the Natural Science Foundation of Shanghai under Grant 22ZR1434600, in part by the Shanghai Sailing Program under Grant 19YF1424100, in part by the Open Research Fund of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources under Grant MESTA-2020-B008, and in part by the Key Prospective Research Fund of Shanghai Jiao Tong University under Grant 2020QY10.

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Authors and Affiliations

  1. School of Oceanography, Shanghai Jiao Tong University, Shanghai, 200030, China

    Yiming Zhong, Caoyang Yu, Rui Wang, Tianqi Pei & Lian Lian

  2. Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou, China

    Caoyang Yu

  3. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Lian Lian

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  1. Yiming Zhong
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  2. Caoyang Yu
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Correspondence to Caoyang Yu.

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Zhong, Y., Yu, C., Wang, R. et al. Adaptive Anti-noise Least-Squares Algorithm for Parameter Identification of Unmanned Marine Vehicles: Theory, Simulation, and Experiment. Int. J. Fuzzy Syst. 25, 369–381 (2023). https://doi.org/10.1007/s40815-022-01424-7

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  • DOI: https://doi.org/10.1007/s40815-022-01424-7

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