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Identification method of nonlinear maneuver model for unmanned surface vehicle from sea trial data based on support vector machine

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Abstract

In order to solve the difficulty of modeling the unmanned surface vehicle (USV) nonlinear maneuver model, a combination identification method of linear hydrodynamic coefficients and nonlinear hydrodynamic coefficients based on support vector machine (SVM) is proposed. The identification principle of USV hydrodynamic coefficients is briefly introduced and a regression algorithm of the SVM is derived for the USV maneuver model. Then, the linear hydrodynamic coefficients of the hull are identified by using a series of USV turning test data at small water-jet angles. And the large water-jet angle turning motion test data and the identified linear hydrodynamic coefficients are used to identify the nonlinear hydrodynamic coefficients for USV. The fourth-order Runge-Kutta method is used to design the USV maneuver simulation program, and a series of USV turning motion simulation experiments are carried out. The simulation data is compared with the corresponding USV sea trial data. Through comparative analysis, it is shown that the USV maneuver mathematical model established in this paper can describe the maneuverability of the USV. It is feasible to use the combination method of SVM to identify the hydrodynamic coefficient of USV.

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Acknowledgments

This work was funded by Research Fund from Science and Technology on Underwater Vehicle Technology Laboratory (Grant number 6142215190104), and National Natural Science Foundation of China (Grant number 51309148).

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Authors and Affiliations

  1. College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Gongxing Wu, Jiawei Zhang, Guofu Li, Linling Wang, Qiang Yu & Jiamin Guo

Authors
  1. Gongxing Wu
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  2. Jiawei Zhang
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  3. Guofu Li
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  4. Linling Wang
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  5. Qiang Yu
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  6. Jiamin Guo
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Corresponding author

Correspondence to Gongxing Wu.

Additional information

Gongxing Wu is an Assistant Professor of the College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China. He received his Ph.D. in Naval Architecture and Marine Engineering from Harbin Engineering University. His research interests include ship maneuvering, system identification, intelligent control. His aim is to improve the intelligent port automation, ship navigation performance and automation level.

Jiawei Zhang is currently pursuing the Master’s degree in Shanghai Maritime University (SMU), Shanghai, China. His research direction is the informatization design of unmanned surface vehicle.

Guofu Li is currently pursuing the Master’s degree in Shanghai Maritime University (SMU), Shanghai, China. His research direction is the autonomous avoidance of surface drones.

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Wu, G., Zhang, J., Li, G. et al. Identification method of nonlinear maneuver model for unmanned surface vehicle from sea trial data based on support vector machine. J Mech Sci Technol 36, 4257–4267 (2022). https://doi.org/10.1007/s12206-022-0743-0

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  • DOI: https://doi.org/10.1007/s12206-022-0743-0

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