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Research on parameter identification method of unmanned surface vehicle motion model

Published: 18 November 2024 Publication History

Abstract

In order to solve the issues of maneuverability prediction and motion model accuracy for unmanned boats, a motion experiment for unmanned boat maneuverability was designed, and a parameter identification method based on recursive least squares method was proposed. By designing unmanned boat maneuvering motion experiments and using recursive least squares method for parameter identification, the response model parameters of the "Jiangfu-16" unmanned boat were accurately estimated. During the identification process, parameters a and b were obtained that were close to the theoretical values. The relative errors of the rotation and Z-shaped identification of value a were 0.98% and 1.07%, respectively, while the relative errors of value b were 1% and 7%, respectively. Based on the identified parameters, a heading control system model was constructed and simulated on the MATLAB/Simulink platform. The output heading angle of the model was highly consistent with the actual ship navigation data, indicating that the proposed method can effectively simulate the changes in the heading angle of unmanned boats, verifying the practicality and accuracy of the model. The research results show that the proposed parameter identification method improves the accuracy of unmanned boat maneuverability prediction and motion model, providing a method for autonomous navigation control of unmanned boats.

References

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LIAO, YULEI, JIANG, QUANQUAN, DU, TINGPENG, et al. Redefined Output Model-Free Adaptive Control Method and Unmanned Surface Vehicle Heading Control[J]. IEEE Journal of Oceanic Engineering: A Journal Devoted to the Application of Electrical and Electronics Engineering to the Oceanic Environment, 2020, 45(3): 714-723.
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ZHANG Lizhen, GAO Hao, WU et al. Research on trajectory tracking control of semi-submersible unmanned vehicle navigation based on MPC[J]. GNSS World of China, 2020, 45(03): 63-70.
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Junxiang Hu, Yuan Ge, Xu Zhou, et al. Research on the course control of USV based on improved ADRC[J]. Systems Science & Control Engineering, 2020, 9(1): 44-51.
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DENG Jun, ZHONG Lun-chao. Research on the automatic maneuverability of unmanned boat based on computer aided technology [J]. Ship Science and Technology, 2020, 42(02):37-39.
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Chu Shixin, Mao Yunsheng, Dong Zaopeng, Yang Xin, Huang Cheng. Parameter identification of unmanned boat maneuvering response model based on UKF [J]. Journal of Wuhan University of Technology (Transportation Science and Engineering Edition), 2019, 43 (05): 947-950+956.
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Hu Changqing, Zhao Jingrui, Sun Xuejiao, Li Qingzhou, Tang Junwu. Identification method of unmanned boat model based on auxiliary variable least squares [J]. Navigation and Control, 2021, 20 (01): 78-85.
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FOSSEN T I. Handbook of marine craft hydrodynamics and motion control[M]. New York: John Wiley & Sons, 2011.
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Hu Changqing, Zhao Jingrui, Sun Xuejiao, Li Qingzhou, Tang Junwu. Identification method of unmanned boat model based on auxiliary variable least squares [J]. Navigation and Control, 2021-20 (01): 78-85.

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  1. Research on parameter identification method of unmanned surface vehicle motion model

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    ICCIR '24: Proceedings of the 2024 4th International Conference on Control and Intelligent Robotics
    June 2024
    399 pages
    ISBN:9798400709937
    DOI:10.1145/3687488
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 November 2024

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    Author Tags

    1. Motion model
    2. Parameter identification
    3. Recursive least squares
    4. Unmanned boat

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