Identification of Unmanned Surface Vehicle Maneuverability Parameters Based on Improved WLSSVM

ZHANG Haisheng; DONG Zaopeng; YANG Lian; ZHANG Zhengqi; QI Shijie; LI Jiakang

doi:10.11993/j.issn.2096-3920.2022-0044

Volume 31 Issue 5

Oct 2023

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Article Navigation > Journal of Unmanned Undersea Systems > 2023 > 31(5): 687-695

ZHANG Haisheng, DONG Zaopeng, YANG Lian, ZHANG Zhengqi, QI Shijie, LI Jiakang. Identification of Unmanned Surface Vehicle Maneuverability Parameters Based on Improved WLSSVM[J]. Journal of Unmanned Undersea Systems, 2023, 31(5): 687-695. doi: 10.11993/j.issn.2096-3920.2022-0044

Citation:

ZHANG Haisheng, DONG Zaopeng, YANG Lian, ZHANG Zhengqi, QI Shijie, LI Jiakang. Identification of Unmanned Surface Vehicle Maneuverability Parameters Based on Improved WLSSVM[J]. Journal of Unmanned Undersea Systems, 2023, 31(5): 687-695. doi: 10.11993/j.issn.2096-3920.2022-0044

Citation:

ZHANG Haisheng, DONG Zaopeng, YANG Lian, ZHANG Zhengqi, QI Shijie, LI Jiakang. Identification of Unmanned Surface Vehicle Maneuverability Parameters Based on Improved WLSSVM[J]. Journal of Unmanned Undersea Systems, 2023, 31(5): 687-695. doi: 10.11993/j.issn.2096-3920.2022-0044

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Identification of Unmanned Surface Vehicle Maneuverability Parameters Based on Improved WLSSVM

doi: 10.11993/j.issn.2096-3920.2022-0044

ZHANG Haisheng^{1, 2
,},
DONG Zaopeng^{1, 2
,
,},
YANG Lian³,
ZHANG Zhengqi^{1, 2
,},
QI Shijie^{1, 2
,},
LI Jiakang^{1, 2
,}

1.
Key Laboratory of High Performance Ship Technology of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China
2.
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
3.
China Institute of Marine Technology & Economy, China State Shipbuilding Corporation Limited, Beijing 100081, China

Received Date: 2022-08-09
Accepted Date: 2022-09-27
Rev Recd Date: 2022-09-13

Available Online: 2023-10-12

Abstract

Abstract

A cosine processing method was designed to achieve high-precision identification modeling of unmanned surface vehicle(USV) maneuvering motion and address the issue that some parameters will be inaccurately identified when the second-order nonlinear response model of USVs is identified by least square support vector machine(LSSVM). On this premise, a weighted LSSVM(WLSSVM) algorithm that could optimize the weight was proposed. The algorithm was based on the idea of data weighting and used the adaptive particle swarm optimization technique with a mutation approach. Based on simulation data and actual ship data, the identification results indicate that the model after cosine reconstruction effectively handles the problem of inaccurate parameter identification. At the same time, the WLSSVM with optimized weights has better prediction accuracy for parameter identification modeling. The research findings can serve as a reference for high-precision parameter identification modeling of USV maneuvering motion.
- unmanned surface vehicle,
- motion parameter identification,
- least squares support vector machine,
- adaptive particle swarm

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References(27)

References

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