Skip to main content
SpringerLink
Log in

Online interactive identification method based on ESO disturbance estimation for motion model of double propeller propulsion unmanned surface vehicle

  • Research Article
  • Published:
Control Theory and Technology Aims and scope Submit manuscript

Abstract

In this paper, the online parameter identification problem of the mathematical model of an unmanned surface vehicle (USV) considering the characteristics of the actuator is studied. A data-driven mathematical model of motion is very meaningful to realize trajectory prediction and adaptive motion control of the USV. An interactive identification algorithm (ESO–MILS, extended state observer–multi-innovation least squares) based on ESO is proposed. The robustness of online identification is improved by expanding the state observer to estimate the current disturbance without making artificial assumptions. Specifically, the three-degree-of-freedom dynamic equation of the double propeller propulsion USV is constructed. A linear model for online identification is derived by parameterization. Based on the least square criterion function, it is proved that the interactive identification method with disturbance estimation can improve the identification accuracy from the perspective of mathematical expectation. The extended state observer is designed to estimate the unknown disturbance in the model. The online interactive update improves the disturbance immunity of the identification algorithm. Finally, the effectiveness of the interactive identification algorithm is verified by simulation experiment and real ship experiment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Algorithm 1
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29

Similar content being viewed by others

References

  1. Zhang, W., Liu, X., & Han, P. (2020). Progress and challenges of overwater unmanned systems. Acta Automatica Sinica, 46(5), 847–857.

    Google Scholar 

  2. Zhang, X., Wang, X., Meng, Y., & Yin, Y. (2021). Research progress and future development trend of ship motion modeling and simulation. Journal of Dalian Maritime University, 47, 1–8.

    Google Scholar 

  3. Nguyen, T. (2022). Identification modeling and steering controller design for unmanned surface vehicles. In: 2022 6th International Conference on Green Technology and Sustainable Development (GTSD) (pp. 8–12). IEEE.

  4. Qin, Y., & Ma, Y. (2014). Parametric identification of ship’s maneuvering motion based on improved least square method. In: 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC-14) (pp. 773–778). Atlantis Press.

  5. Sun, G., Xie, J., & Wang, J. (2018). Ship course identification model based on recursive least squares algorithm with dynamic forgetting factor. Journal of Computer Applications, 38(3), 900.

    Google Scholar 

  6. Zhao, Y., Wu, J., Zeng, C., & Huang, Y. (2022). Identification of hydrodynamic coefficients of a ship manoeuvring model based on prbs input. Ocean Engineering, 246, 110640.

    Article  Google Scholar 

  7. Guan, W., Peng, H., Zhang, X., & Sun, H. (2022). Ship steering adaptive cgs control based on ekf identification method. Journal of Marine Science and Engineering, 10(2), 294.

    Article  Google Scholar 

  8. Qin, C. (2021). Parameter identification for ship mathematical model based on unscented kalman filter. Ship Science and Technology, 43(01), 89–94.

    Google Scholar 

  9. Zheng, J., Yan, D., Yan, M., Li, Y., & Zhao, Y. (2022). An unscented kalman filter online identification approach for a nonlinear ship motion model using a self-navigation test. Machines, 10(5), 312.

    Article  Google Scholar 

  10. Hu, Y., Song, L., Liu, Z., & Yao, J. (2021). Identification of ship hydrodynamic derivatives based on LS-SVM with wavelet threshold denoising. Journal of Marine Science and Engineering, 9(12), 1356.

    Article  Google Scholar 

  11. Wang, Z., Zou, Z., & Soares, G. (2019). Identification of ship manoeuvring motion based on nu-support vector machine. Ocean Engineering, 183, 270–281.

    Article  Google Scholar 

  12. Silva, K. M., & Maki, K. J. (2022). Data-driven system identification of 6-DOF ship motion in waves with neural networks. Applied Ocean Research, 125, 103222.

    Article  Google Scholar 

  13. Wakita, K., Maki, A., Umeda, N., Miyauchi, Y., Shimoji, T., Rachman, D. M., & Akimoto, Y. (2022). On neural network identification for low-speed ship maneuvering model. Journal of Marine Science and Technology, 27(1), 772–785.

    Article  Google Scholar 

  14. Ding, F., Xie, X., & Fang, C. (1996). Multi-innovation identification method for time-varying systems. Acta Automatica Sinica, 22, 85–91.

    Google Scholar 

  15. Xie, S., Chu, X., Liu, C., & Wu, Q. (2017). Parameter identification of ship maneuvering response model based on multi-innovation least squares algorithm. Navigation of China, 40(1), 73–78.

    Google Scholar 

  16. Zhang, X., & Zhu, H. (2021). New identification algorithm for ship model parameters based on sinusoidal function processing innovation. Chinese Journal of Ship Research, 16(5), 158–162.

    Google Scholar 

  17. Zhao, B., Zhang, X., & Liang, C. (2022). A novel parameter identification algorithm for 3-DOF ship maneuvering modelling using nonlinear multi-innovation. Journal of Marine Science and Engineering, 10(5), 581.

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Mei, B., Sun, L., & Shi, G. (2019). White-black-box hybrid model identification based on rm-rf for ship maneuvering. IEEE Access, 7, 57691–57705.

    Article  Google Scholar 

  20. Mu, D., Wang, G., Fan, Y., & Zhao, Y. (2017). Modeling and identification of podded propulsion unmanned surface vehicle and its course control research. Mathematical Problems in Engineering, 2017, 3209451.

    Article  MathSciNet  Google Scholar 

  21. Hemati, N., & Leu, M. C. (1992). A complete model characterization of brushless dc motors. IEEE Transactions on Industry Applications, 28(1), 172–180.

    Article  Google Scholar 

  22. Cao, H., Xu, R., Zhao, S., Li, M., Song, X., & Dai, H. (2022). Robust trajectory tracking for fully-input-bounded actuated unmanned surface vessel with stochastic disturbances: An approach by the homogeneous nonlinear extended state observer and dynamic surface control. Ocean Engineering, 243, 110113.

    Article  Google Scholar 

  23. Fossen, T. I. (2011). Handbook of marine craft hydrodynamics and motion control (pp. 109–132). Hoboken: Wiley.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Navigation, Wuhan University of Technology, Wuhan, 430063, Hubei, China

    Yong Xiong, Xianfei Wang & Siwen Zhou

  2. National Engineering Research Center for Water Transport Safety, Wuhan, 430063, Hubei, China

    Yong Xiong

  3. Hubei Inland Technology Key Laboratory, Wuhan, 430063, Hubei, China

    Yong Xiong

Authors
  1. Yong Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianfei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siwen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Xiong.

Additional information

This work was supported by the National Natural Science Foundation of China (No. 52271367).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiong, Y., Wang, X. & Zhou, S. Online interactive identification method based on ESO disturbance estimation for motion model of double propeller propulsion unmanned surface vehicle. Control Theory Technol. (2024). https://doi.org/10.1007/s11768-024-00201-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11768-024-00201-1

Keywords

Search

Navigation