Volume 23 Issue 6
Dec.  2023
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TUO Yu-long, KANG Cai-xia, WANG Sha-sha, DAI Dong-chen, GAO Shuang, LI Li-li. Adjacent cross-coupling synchronous formation control with collision avoidance for multiple ships under unknown disturbances[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 314-326. doi: 10.19818/j.cnki.1671-1637.2023.06.021
Citation: TUO Yu-long, KANG Cai-xia, WANG Sha-sha, DAI Dong-chen, GAO Shuang, LI Li-li. Adjacent cross-coupling synchronous formation control with collision avoidance for multiple ships under unknown disturbances[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 314-326. doi: 10.19818/j.cnki.1671-1637.2023.06.021
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Adjacent cross-coupling synchronous formation control with collision avoidance for multiple ships under unknown disturbances

doi: 10.19818/j.cnki.1671-1637.2023.06.021
  • 1.

    College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

  • 2.

    School of Mechanical and Electrical Engineering, Shaoxing University, Shaoxing 312000, Zhejiang, China

Funds:

National Natural Science Foundation of China 52101298

National Natural Science Foundation of China 52201409

China Postdoctoral Science Foundation 2019M661082

Fundamental Research Funds for the Central Universities 3132022104

Natural Science Foundation of Zhejiang Province LQ22E090007

Dalian Innovative Support Project for High-Level Talents 2023RQ066

More Information
  • Author Bio:

    TUO Yu-long(1990-), male, associate professor, PhD, tuoyulong@dlmu.edu.cn

  • Received Date: 2023-06-17
  • Publish Date: 2023-12-25
    Abstract
  • In view of the poor synchronization performance and obstacle collision risk during formation navigation under unknown marine disturbances, a distributed adjacent cross-coupling synchronous formation robust control method with collision avoidance was proposed for multiple ships, and an adjacent cross-coupling synchronous control strategy was proposed to achieve higher synchronous control accuracy, and the unknown marine disturbances were estimated by the neural network. In order to effectively prevent collision risks between ships and obstacles, as well as between ships, the artificial potential field method was applied to the multi-ship formation control system. The effectiveness of the proposed method was tested by simulating the parallel formation navigation scenarios of five ships facing multiple obstacles and unknown marine disturbances. Research results show that all ships can navigate in the expected formation after safely avoiding the obstacles in considering obstacles and external marine disturbances. After about 9 s, the ships can reach consistent velocities. Although the velocities of the ships fluctuate slightly when there are obstacles, the ships are able to continue navigating with consistent velocity after 30 s. Besides, there also exist small oscillations in the position and velocity tracking errors, position synchronization errors of adjacent ships, and neural network approximation errors. However, these errors can eventually converge to 0 after 30 s, ensuring the synchronization of the position and velocity information of the five ships. Therefore, the proposed method can not only solve the problem of poor synchronization performance when ships are sailing in formation under unknown marine disturbances, but also effectively reduce the collision risk between ships and obstacles, as well as between ships. To some extent, the autonomy and safety of the ships during formation navigation have been improved.

     

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    • References(29)
  • [1]
    PENG Zhou-hua, WANG Jun, WANG Dan, et al. An overview of recent advances in coordinated control of multiple autonomous surface vehicles[J]. IEEE Transactions on Industrial Informatics, 2021, 17(2): 732-745. doi: 10.1109/TII.2020.3004343
    [2]
    LU Yu. Adaptive formation control for the cooperation of autonomous surface vehicles[D]. Shanghai: Shanghai Jiao Tong University, 2022. (in Chinese)
    [3]
    LIU Chen-guang, CHU Xiu-min, WU Qing, et al. A review and prospect of USV research[J]. Shipbuilding of China, 2014, 55(4): 194-205. (in Chinese) doi: 10.3969/j.issn.1000-4882.2014.04.024
    [4]
    HOU Rui-chao, TANG Zhi-cheng, WANG Bo, et al. Development status and trend of intelligent technology for unmanned surface vehicles[J]. Shipbuilding of China, 2020, 61(S1): 211-220. (in Chinese)
    [5]
    LIU Chen-guang, HE Zhi-bo, CHU Xiu-min, et al. Overview on ship formation control[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 10-27. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2022.04.002
    [6]
    TUO Yu-long, WANG Sha-sha, PENG Zhou-hua, et al. Reliability-based fixed-time nonsingular terminal sliding mode control for dynamic positioning of turret-moored vessels with uncertainties and unknown disturbances[J]. Ocean Engineering, 2022, 248: 110748. doi: 10.1016/j.oceaneng.2022.110748
    [7]
    LIANG Xiao, QU Xing-ru, WANG Ning, et al. A novel distributed and self-organized swarm control framework for underactuated unmanned marine vehicles[J]. IEEE Access, 2019, 7: 112703-112712. doi: 10.1109/ACCESS.2019.2934190
    [8]
    LU Yu, ZHANG Guo-qing, SUN Zhi-jian, et al. Adaptive cooperative formation control of autonomous surface vessels with uncertain dynamics and external disturbances[J]. Ocean Engineering, 2018, 167: 36-44. doi: 10.1016/j.oceaneng.2018.08.020
    [9]
    MA Yu-fei. Formation control and learning of multiple unmanned surface vehicles based on deterministic learning method[D]. Guangzhou: South China University of Technology, 2020. (in Chinese)
    [10]
    FU Ming-yu, JIAO Jian-fang, YIN Shen. Robust coordinated formation for multiple surface vessels based on backstepping sliding mode control[J]. Abstract and Applied Analysis, 2013, 2013: 681319.
    [11]
    SUN Dong, WANG Can, SHANG Wen, et al. A synchronization approach to trajectory tracking of multiple mobile robots while maintaining time-varying formations[J]. IEEE Transactions on Robotics, 2009, 25(5): 1074-1086. doi: 10.1109/TRO.2009.2027384
    [12]
    LI Le-bao, SUN Ling-ling, ZHANG Sheng-zhou. Mean deviation coupling synchronous control for multiple motors via second-order adaptive sliding mode control[J]. ISA Transactions, 2016, 62: 222-235. doi: 10.1016/j.isatra.2016.01.015
    [13]
    YANG Li-wei, FU Li-xia, LI Ping. Summary of development of multi-agent system formation control[J]. Electronic Measurement Technology, 2020, 43(24): 18-27. (in Chinese)
    [14]
    ZHANG Ying, LI Xu. Leader-follower formation control and obstacle avoidance of multi-robot based on artificial potential field[C]//IEEE. The 27th Chinese Control and Decision Conference (2015 CCDC). New York: IEEE, 2015: 4355-4360.
    [15]
    DING Guo-hua, ZHU Da-qi, SUN Bing. Formation control and obstacle avoidance of multi-AUV for 3-D underwater environment[C]//IEEE. Proceedings of the 33rd Chinese Control Conference. New York: IEEE, 2014: 8347-8352.
    [16]
    ZHOU Ding-jiang, WANG Zi-jian, SCHWAGER M. Agile coordination and assistive collision avoidance for quadrotor swarms using virtual structures[J]. IEEE Transactions on Robotics, 2018, 34(4): 916-923. doi: 10.1109/TRO.2018.2857477
    [17]
    CHEN Xuan-lin, HUANG Fang-hao, ZHANG You-gong, et al. A novel virtual-structure formation control design for mobile robots with obstacle avoidance[J]. Applied Sciences, 2020, 10(17): 5807. doi: 10.3390/app10175807
    [18]
    LIN Jin-ling, HWANG K S, WANG Ya-ling. A simple scheme for formation control based on weighted behavior learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(6): 1033-1044. doi: 10.1109/TNNLS.2013.2285123
    [19]
    XU Xiao-hao, LI Cheng-gong, ZHAO Yi-fei, et al. Rerouting path planning based on artificial potential field algorithm[J]. Journal of Traffic and Transportation Engineering, 2009, 9(6): 64-68. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2009.06.013
    [20]
    KHATIB O. Real-time obstacle avoidance system for manipulators and mobile robots[C]//IEEE. Proceedings of 1985 IEEE International Conference on Robotics and Automation. New York: IEEE, 2003: 500-505.
    [21]
    GUO Yin-jing, LIU Qi, BAO Jian-kang, et al. Overview of AUV obstacle avoidance algorithm based on artificial potential field method[J]. Journal of Computer Engineering and Applications, 2020, 56(4): 16-23. (in Chinese)
    [22]
    LIU Kun, ZHANG Yong-hui, REN Jia. Path planning algorithm for unmanned surface vehicle based on an improved artificial potential field method[J]. Natural Science Journal of Hainan University, 2016, 34(2): 99-104. (in Chinese)
    [23]
    LAZAROWSKA A. A new potential field inspired path planning algorithm for ships[C]//IEEE. 2018 23rd International Conference on Methods and Models in Automation and Robotics (MMAR). New York: IEEE, 2018: 166-170.
    [24]
    LYU Hong-guang, YIN Yong. Ship's trajectory planning for collision avoidance at sea based on modified artificial potential field[C]//IEEE. 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE). New York: IEEE, 2018: 351-357.
    [25]
    CHI Cen. Research on autonomous collision avoidance method of USV based on machine learning method[D]. Harbin: Harbin Engineering University, 2016. (in Chinese)
    [26]
    WANG Jia, WU Xiao-bei, XU Zhi-liang. Decentralized formation control and obstacles avoidance based on potential field method[C]//IEEE. 2006 International Conference on Machine Learning and Cybernetics. New York: IEEE, 2009: 803-808.
    [27]
    WANG Yi-fan, SUN Xing-yan. Formation control of multi-UAV with collision avoidance using artificial potential field[C]// IEEE. 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). New York: IEEE, 2019: 296-300.
    [28]
    XU Yu-jie. Research on collision avoidance control methods for multiple dynamic positioning ships[D]. Harbin: Harbin Engineering University, 2018. (in Chinese)
    [29]
    WANG Yong-jun, ZHENG Li. Proof of the Young inequality and the Schwarz inequality[J]. Mathematics Learning and Research, 2020(26): 145-147. (in Chinese)
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