Chinese Journal of Ship Research, Volume. 16, Issue 1, 105(2021)

Tracking control of intelligent ship based on deep reinforcement learning

Kang ZHU1, Zhen HUANG1, and Xuming WANG2
Author Affiliations
  • 1School of Automation, Wuhan University of Technology, Wuhan 430070, China
  • 2Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan 430063, China
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    References(11)

    [4] [4] LIU S, XING B W, ZHU W L. A fusion fuzzy PID controller with realtime implementation on a ship course control system[C]Proceedings of the 2015 23rd Mediterranean Conference on Control Automation (MED). Tremolinos, Spain: IEEE, 2015.

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    [7] [7] WOO J, KIM N. Vect field based guidance method f docking of an unmanned surface vehicle[C]Proceedings of the 12th ISOPE PacificAsia Offshe Mechanics Symposium. Gold Coast, Australia: International Society of Offshe Polar Engineers, 2016.

    [9] MOREIRA L, FOSSEN T I, SOARES C G. Path following control system for a tanker ship model[J]. Ocean Engineering, 34, 2074-2085(2007).

    [11] [11] CARRERAS M, RIDAO P, ELFAKDI A. Semionline neural Q_leaming f realtime robot learning[C]Proceedings of 2003 IEEERSJ International Conference on Intelligent Robots Systems. Las Vegas, Nevada: IEEE, 2003: 662667.

    [14] [14] LILLICRAP T P, HUNT J J, PRITZEL A, et al. Continuous control with deep reinfcement learning[C]Proceedings of the 4th International Conference on Learning Representations. San Juan, 2015: A187.

    [15] [15] SILVER D, LEVER G, HEESS N, et al. Deterministic policy gradient algithms[C]Proceedings of the 31st International Conference on Machine Learning. Beijing, China: ACM, 2014: I387–I395.

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    Kang ZHU, Zhen HUANG, Xuming WANG. Tracking control of intelligent ship based on deep reinforcement learning[J]. Chinese Journal of Ship Research, 2021, 16(1): 105

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    Paper Information

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    Received: Apr. 29, 2020

    Accepted: --

    Published Online: Mar. 27, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.01940

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