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Publishing Language: Chinese

Adaptive cascade tracking control of USV for recovery task

Wenxuan GUO1, Guoyuan TANG1( ), Fan ZHAO1, Quanbin WANG2, Jianglong SUN1, Yu QIAO3
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
China Ship Development and Design Center, Wuhan 430064, China
School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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Abstract

Objectives

Aiming at the high-precision recovery guidance control requirements of current stern ramp recovery technology, a self-adaptive cascade tracking control method for unmanned surface vessels (USVs) is proposed specifically for stern ramp recovery.

Methods

Based on the technical requirements of stern ramp recovery, a motion model of an underactuated USV is established, and the generalized Kalman filter (GKF) algorithm is used to predict the navigation state and recovery position of the mother ship. Introducing the idea of constant bearing guidance combined with the sliding mode variable structure control theory, a stable cascade control system is constructed to solve tracking control problems during the recovery process.

Results

It is proven that the USV can stably track the target, by analyzing the stability of the system through the Lyapunov theory and cascade theorem.

Conclusions

The simulation results show that the proposed control method gives the USV stable tracking performance and strong robustness against uncertain disturbances.

Keywords

stern ramp recoveryunderactuated unmanned surface vehiclegeneralized Kalman filterconstant bearing navigationadaptive sliding mode control
CLC number: U661.33 Document code: A

References

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Chinese Journal of Ship Research
Volume 18 Issue 5,
October 2023
Pages 111-120
DOI: 10.19693/j.issn.1673-3185.02882

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Cite this article:
GUO W, TANG G, ZHAO F, et al. Adaptive cascade tracking control of USV for recovery task. Chinese Journal of Ship Research, 2023, 18(5): 111-120. https://doi.org/10.19693/j.issn.1673-3185.02882

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Received: 27 April 2022
Revised: 21 August 2022
Published: 12 March 2023
© 2023 Chinese Journal of Ship Research.