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The Design of Ship Formation Based on a Novel Disturbance Rejection Control

  • Yun Li
  • Jian Zheng
Regular Papers Control Theory and Applications
  • 18 Downloads

Abstract

Ship formation is the common form of maritime transport, reconnaissance, and rescue, which composes the uniform formation through the coordination of individual ship motion, but it is vulnerable to external disturbances in the regulation, causing the floating of position. In view of the interference of ship adjustment, and considering the principles of active disturbance rejection, a novel disturbance rejection control was designed, combining with the artificial potential field algorithm, which applying the feedback of spacing error, to improve the impact of spacing disturbance. And then adjust the speed and course of the individual ship by sliding mode control with disturbance observer, resisting the interference of ship formation and guaranteeing the performance of ship formation. Finally analyzing and comparing the state diachronic trend of ship formation by the simulation verification, the ship state achieves consistency, reaching the goal of ship spacing and maintaining the stability of formation, which obtain good results and verify the effectiveness of algorithm.

Keywords

Artificial potential field disturbance rejection control leader/follower method multi-vessel formation 

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References

  1. [1]
    Y. Liu and M. J. Liu, “Leader/follower formation control of underactuated surface ships,” Control Engineering of China, vol. 20, no. 5, pp. 980–989, November, 2013.Google Scholar
  2. [2]
    C. H. Mou, Coordinated Control of Multi-UUV Formation for Spatial Curve Path Following, Harbin Engineering University, November 2011.Google Scholar
  3. [3]
    A. Filippo and I. F. Thor, “Formation control of underactuated surface vessels using the null-spacing-based behavioral control,” Proc. of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2006.Google Scholar
  4. [4]
    Z. Z. Qin, Z. H. Lin, P. Li, and H. L. Liu, “Formation control of underactuated ships with input saturation,” Journal of Huazhong University of Science and Technology Natural Science Edition, vol. 43, no. 8, pp. 75–78, August 2015.MathSciNetGoogle Scholar
  5. [5]
    P. Ehsan and I. F. Thor, “Leader-follower formation of marine craft using constraint forces and Lagrange multipliers,” Proc. of the 51st IEEE Conference on Decision and Control, December 2012.Google Scholar
  6. [6]
    M. Chao and Q. H. Zeng, “Distributed formation control of 6-DOF autonomous underwater vehicles networked by sampled-data information under directed topology,” Neurocomputing, vol. 154, pp. 33–40, April 2015.CrossRefGoogle Scholar
  7. [7]
    M. Paul, A. K. Christopher, and M. Ignacio, “Dynamic guarding of marine assets through cluster control of automated surface vessel fleets,” IEEE/ASME Transactions on Mechatronics, vol. 17, no. 1, pp. 65–75, December 2012.CrossRefGoogle Scholar
  8. [8]
    S. Khoshnam, “Leader-follower formation control of underactuated autonomous marine surface vehicles with limited torque,” Ocean Engineering, vol. 10, pp. 196–205, September 2015.Google Scholar
  9. [9]
    F. Jiang and H. W. Bian, “The ship formation control algorithm research based on the technology of LADRC,” Digital Technology and Application, vol. 6, pp. 148–149, June 2016.Google Scholar
  10. [10]
    S. P. Hou, R. Bai, Z. P. Yan, and C. Mou, “Path following control of multi-UUV formation disturbed by current,” Shipbuilding of China, vol. 54, no. 4, pp. 126–126, December, April 2013.Google Scholar
  11. [11]
    K. Sun, Z. L. Xu, and J. Y. Zou, “Research on speed estimation of Pmsm based on active-disturbance rejection controller,” Journal of System Simulation, vol. 19, no. 3, pp. 582–584, February 2007.Google Scholar
  12. [12]
    H. Wan, “Absolute stability analysis of active disturbance rejection controller,” Shanghai University of Electric Power, vol. 27, no. 5, pp. 507–511, October 2011.Google Scholar
  13. [13]
    H. Yu, Multi-agent Robot Coordinated Control Study and Stability Analysis, Huazhong University of Science and Technology, May 2007.Google Scholar
  14. [14]
    Y. X. Qin, Y. Li, and G. L. Xu, “Based on artificial potential field and fuzzy rules of underwater robot formation control method,” Computer Measurement and Control, vol. 20, no. 8, pp. 2105–2107, August 2012.Google Scholar
  15. [15]
    Y. C. Liu and B. Richard, “Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment,” Ocean Engineering, vol. 97, pp. 126–144, March 2015.CrossRefGoogle Scholar
  16. [16]
    A. F. Roseli, P. Edson, A. P. Marco, and F. Gedson, “Locally oriented potential field for controlling multi-robots,” Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 12, pp. 4664–4671, December 2012.MathSciNetCrossRefGoogle Scholar
  17. [17]
    X. L. Jia and Y. S. Yang, Mathematical Model of Ship Motion: the Mechanism Modeling and Identification Modeling, Dalian, 1999.Google Scholar
  18. [18]
    S. Dadras, H. R. Momeni, and S. Dadras, “Adaptive control for ship roll motion with fully unknown parameters,” IEEE International Conference on. IEEE, December 2009.Google Scholar
  19. [19]
    Y. Li, X. E. Bai, and Y. J. Xiao, “Ship course sliding mode control system based on a novel extended state disturbance observer,” Journal of Shanghai Jiao Tong University, vol. 48, no. 12, pp. 1708–1713, December 2014.MathSciNetGoogle Scholar
  20. [20]
    Y. S. Yang, “Study on course keeping performance during ship sailing in wind wave,” Navigation of China, vol. 35, no. 2, pp. 36–44, February 1994.Google Scholar
  21. [21]
    Q. Zhou, L. J. Wang, C. W. Wu, and H. Y. Li, “Adaptive fuzzy tracking control for a class of pure-feedback nonlinear systems with time-varying delay and unknown dead zone,” Fuzzy Sets and Systems, vol. 329, pp. 36–60, December 2017.MathSciNetCrossRefzbMATHGoogle Scholar
  22. [22]
    Q. Zhou, H. Y. Li, L. J. Wang, and R. Lu, “Prescribed performance observer-based adaptive fuzzy control for nonstrict-feedback stochastic nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, no. 99, pp. 1–12, September 2017.Google Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Merchant MarineShanghai Maritime UniversityShanghaiChina
  2. 2.School of Transport and CommunicationsShanghai Maritime UniversityShanghaiChina

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