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Cluster Computing

, Volume 21, Issue 1, pp 855–868 | Cite as

Formation path control method for group coordination based on fuzzy logic control method

  • Lanyong ZhangEmail author
  • Zewei Liu
  • Christos Papavassiliou
  • Sheng Liu
Article
  • 142 Downloads

Abstract

Unmanned ship is an effective tool for human to exploit the sea. Single unmanned ship has been far from meeting the complex needs of the development of the ocean. But multiple unmanned ships cooperation system could expand the perception scope of individual unmanned ship, and achieve complex tasks. By adopting leader–follower architecture, the formation control scheme is composed of three vehicles. The objective is to synchronize the motion of the flock and show high performances of cooperative control. The controller uses fuzzy control, however, Fuzzy control not only depends on the fuzzy rules of experts, while scaling factors are important parameters that affect the performance of fuzzy control, and scaling factors are usually obtained by trial and error, so it is necessary to find a quick and effective way to adjust the quantization parameters. This paper uses the intelligent algorithm to optimize the entire controller. The simulation results show that follower unmanned ships move to the leader’s path quickly. The proposed method has fast response speed wave disturbances with strong robustness.

Keywords

Formation path control Unmanned ship Fuzzy logic control GA 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China subsidization project (51579047), the National Key Technology Support Program (2013BAG25B01), the Research Fund for the Doctoral Program of Higher Education (20132304120015), the Doctoral Scientific Research Foundation of Heilongjiang (No. LBH-Q14040), the National Defense Fundamental Research Funds (No. IEP14001), the Open Project Program of State Key Laboratory of Millimeter Waves (K201707), and the Fundamental Research Funds for the Central Universities (HEUCF160414).

References

  1. 1.
    Arcak, M.:“Passivity as a design tool for group coordination”. In: IEEE Transactions on Automatic Control (2006)Google Scholar
  2. 2.
    Ren, W., Beard, R. W. & Atkins, E. M.: “A survey of consensus problems in multi-agent coordination”. In: ‘Proceedings American Control Conference’, pp. 1859–1864. Portland, OR, USA (2005)Google Scholar
  3. 3.
    Do, K.D., Jiang, Z.P., Pan, J.: Global partial-state feedback and output-feedback tracking controllers for underactuated ships. Syst. Control Lett. 54, 1015–1036 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cheng, J., Yi, J., Zhao, D.: “A new fuzzy autopilot for way-point tracking control of ships”. In: IEEE, International Conference on Fuzzy Systems, Canada (2006)Google Scholar
  5. 5.
    Moreira, L., Fossen, T.I., Soares, C.G.: “Path following control system for a tanker ship model. Ocean Eng. 34, 2077–2085 (2007)CrossRefGoogle Scholar
  6. 6.
    Doa, K.D., Jiangb, Z.P., Pana, J.: Robust and adaptive path following of underactuated ships. Automatica 40, 929–944 (2004)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Jiang, Z.P.: Global tracking control of underactuated ships by Lyapunov’s direct method. Automatica 38, 301–309 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Do, K.D., Pan, J.: Global Robust adaptive path following of underactuated ships. Automatica 42, 1713–1722 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Fahimi, F.: Sliding-mode formation control for underactuated surface ships. IEEE Trans. Robot. 23(3), 617–622 (2007)CrossRefGoogle Scholar
  10. 10.
    Borhaug, E., Pavlov, A., Pettersen, K.Y.: “Cross-track formation control of underactuated autonomous underwater vehicles”. Group coordination and cooperative control, number 336 in ‘Lecture notes in control and information Sciences’, pp. 35–54. Springer, BerlinGoogle Scholar
  11. 11.
    Ghabcheloo, R.: “Coordinated Path Following of Autonomous Vehicles”. PhD Dissertation of Instituto Superior Técnico, Technical University of Libson, Portugal, May, 2007Google Scholar
  12. 12.
    Ihle, I.A., Jouffroy, J., Fossen, T.I.: “Formation control of marine surface craft using Lagrange multipliers”. In: IEEE conference on decision and control, and the European control conference, pp. 752–758 (2005)Google Scholar
  13. 13.
    Feng, X., Zou, R., Yu, H.: “A novel optimization algorithm inspired by the creative thinking process”. Soft Comput. 19(10), 1–18 (2014)Google Scholar
  14. 14.
    Gong, D., Zhang, Y.: Generating test data for both path coverage and fault detection using genetic algorithm. Front. Comput. Sci. 7(6), 822–837 (2013)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Li, Q., Wang, L.J., Chen, B., Zhou, Z.: “An improved artificial potential field method with parameters optimization based on genetic algorithms”, Beijing Keji Daxue Xuebao/journal of University of Science & Technology, Beijing, 2(2), (2012)Google Scholar
  16. 16.
    Dorronsoro, B., Bouvry, P.: “Study of different small-world topology generation mechanisms for Genetic Algorithms”. IEEE Cong. Evolut. Comput. 36(1–2), 88–90 (2012)Google Scholar
  17. 17.
    Esfahani, R.T., Zojaji, Z.: Optimization of finite element model of laser forming in circular path using genetic algorithms and ANFIS. Soft Comput. 20(5), 2031–2045 (2015)CrossRefGoogle Scholar
  18. 18.
    Salazar, C.A., Enriquez, A.C.: Coordination of overcurrent relays using Genetic Algorithms and unconventional curve. IEEE Lat. Am. Trans. 12(8), 1449–1455 (2014)CrossRefGoogle Scholar
  19. 19.
    Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and genetic programming—modern concepts and practical applications. Parallel Process. Lett. 7(4), 358–365 (2011)zbMATHGoogle Scholar
  20. 20.
    Linkens, D.A., Nyongesa, H.O.: Genetic Algorithms for fuzzy control. IEEE Proc. Control Theor., Appl. 142, 161–185 (1995)CrossRefzbMATHGoogle Scholar
  21. 21.
    Bonissone, p.P., Khedkar, P.S., Chen, Y.: “Genetic algorithms for automated tuning of fuzzy controllers: a transportation application [C]”. In: Proceedings of the 5th IEEE International conference on fuzzy systems, vol 1, pp. 674–680. (1996)Google Scholar
  22. 22.
    Kinzel, J., F. Klawoon, F., Kruse, R.: “Modifications of genetic algorithms for designing and optimizing fuzzy controllers [C]”. In: Proceedings First IEEE Conf. on Evolutionary Computing (ICEC’94)Google Scholar
  23. 23.
    Nair, D.S., Reshma, S.:“Optimal coordination of protective relays”. In: International Conference on Power, Energy & Control, pp. 239–244 (2013)Google Scholar
  24. 24.
    Surmann, H., Kanstein, A., Goser, K.: “Self organizing and genetic algorithms for an automatic design of fuzzy control and decision systems [C]”. In: Proceedings EUFIT’93, pp. 1097-1104 (1993)Google Scholar
  25. 25.
    Houck, C.R., Joines, J., Kay, M.G.: “A genetic algorithm for function optimization: A Matlab implementation [J]”. ACM Trans. Math. Softw. (1996)Google Scholar
  26. 26.
    Šulek, P.: A hydro power system operation using Genetic Algorithms and mixed-integer nonlinear programming. Slovak J. Civil Eng. 20(1), 1–9 (2012)Google Scholar
  27. 27.
    Liu, S., Li G.Y., Song, J., Sun, T.Y.: “Research on optimization efficiency of genetic algorithms”. In: 2nd International Symposium on Systems and Control in Aerospace and Astronautics, ISSCAA (2008)Google Scholar
  28. 28.
    Liu, Y., Yang, Z., Ning, T., Wu, H.: Efficient quality-of-service (QoS) support in mobile opportunistic networks. IEEE Trans. Vehicular Technol. 63, 4574–4584 (2014)CrossRefGoogle Scholar
  29. 29.
    Skjetne, R., Fossen, T.I. : “Nonlinear maneuvering and control of ships”. In: MTS/IEEE Conference and Exhibition, pp. 1808–1815 (2001)Google Scholar
  30. 30.
    Mazenc, F., Pettersen, K.Y., Nijmeijer, H.: Global uniform asymptotic stabilization of an underactuated surface ship. IEEE Trans. Autom. Control 47(10), 1759–1762 (2002)CrossRefzbMATHGoogle Scholar
  31. 31.
    Singh, D.K., Gupta, .: “Use of genetic algorithms (GA) for optimal coordination of directional over current relays”. In: Conference on Engineering & Systems, pp. 1–5 (2012)Google Scholar
  32. 32.
    Yamchi, M.H., Esfanjani, R.M.: Distributed predictive formation control of networked mobile robots subject to communication delay. Robot. Autonomous Syst. 91, 194–207 (2016)CrossRefGoogle Scholar
  33. 33.
    Petrinic, T., Brezak, M., Petrovic, I.: “Time-optimal velocity planning along predefined path for static formations of mobile robots”. The international journal of control, automation, and systems, vol. 5, No. 1, pp. 293–302Google Scholar
  34. 34.
    Zheng, Z.W.: Adaptive integral LOS path following for an unmanned airship with uncertainties based on robust RBFNN backstepping. ISA Trans. 65, 210–219 (2016)CrossRefGoogle Scholar
  35. 35.
    Liu, Y.C., Bucknall, R.: The angle guidance path planning algorithms for unmanned surface vehicle formations by using the fast marching method. Appl. Ocean Res. 59, 327–344 (2016)CrossRefGoogle Scholar
  36. 36.
    Garcia-Pulido, J.A., Pajares, G., Dormido, S., de la Cruz, J.M.: “Recognition of a landing platform for unmanned aerial vehicles by using computer vision-based techniques”, vol. 76, pp. 152–165 (2017)Google Scholar
  37. 37.
    Zhu, J., Wang, J.H., Zheng, T.Q., Wu, G.X.: “Straight path following of unmanned surface vehicle under flow disturbance”, OCEANS Conference, Shanghai (2016)Google Scholar
  38. 38.
    Do, K.D., Pan, J.: “Control of Ships and Underwater Vehicles, Design for Underactuated and Nonlinear Marine Systems”. School of Mechanical Engineering, Crawly, Australia (2009)Google Scholar
  39. 39.
    Ruano, A.E.: “Intelligent control system using computational intelligence technology”, by the Institution of Engineering and technology, UK (2008)Google Scholar
  40. 40.
    Ahmed, F., Deb, K.: “Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms”. Soft Comput. 17(7), 1283–1299 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Lanyong Zhang
    • 1
    Email author
  • Zewei Liu
    • 1
  • Christos Papavassiliou
    • 2
  • Sheng Liu
    • 1
  1. 1.College of AutomationHarbin Engineering UniversityHarbinChina
  2. 2.Electrical and Electronic EngineeringImperial College LondonLondonUK

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