Neural Network Based Direct Adaptive Backstepping Method for Fin Stabilizer System

  • Weiwei Bai
  • Tieshan Li
  • Xiaori Gao
  • Khin Thuzar Myint
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7952)


Based on the backstepping method and the neural networks (NNs) technique, a direct adaptive controller is proposed for a class of nonlinear fin stabilizer system in this paper. This approach overcomes the uncertainty in the nonlinear fin stabilizer system and solves the problems of mismatch and controller singularity. The stability analysis shows that all the signals of the closed-loop system are uniformly ultimate boundedness (UUB). A simulation example is given to illustrate the effectiveness of the proposed method.


fin stabilizer direct adaptive control neural networks backstepping 


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  1. 1.
    Karakas, S.C., Sc, M.: Control design of fin roll stabilization in beam seas based on Lyapunov’s direct method. Maritime Research 73(2), 25–30 (2012)Google Scholar
  2. 2.
    Allan, J.F.: Stabilisation of ships by activated fins. Transactions of the Royal Institution of Naval Architects RINA 87, 123–159 (1945)Google Scholar
  3. 3.
    Conolly, J.E.: Rolling and its stabilization by active fins. Transactions of the Royal Institution of Naval Architects RINA 111, 21–48 (1968)Google Scholar
  4. 4.
    Hickey, N.A., Johnson, M.A., Katebi, M.R., Grimble, M.J.: PID Controller Optimisation for Fin Roll Stabilisation. In: International Conference on Control Applications Kohala Coast-bland of Hawai’i, Hawai’i, USA, vol. 2, pp. 1785–1790 (1999)Google Scholar
  5. 5.
    Wang, X., Zhang, X.: Fin stabilizer control based on backstepping and closed-loop gain shaping algorithms. Journal of Dalian Maritime University 3(34), 89–92 (2008)zbMATHGoogle Scholar
  6. 6.
    Fuat, A.: Internal model control using neural network for ship roll stabilization. Journal of Marine Science and Technology 15(2), 141–147 (2007)Google Scholar
  7. 7.
    Kang, T.Z., Luo, D., Yan, X., Niu, Y.: The Study of Neural Network Control System of the Fin Stabilizer. Ship Egineering 34(2), 278–301 (2012)Google Scholar
  8. 8.
    Fang, M., Zhuo, Y.: The application of the self-tuning neural network PID controller on the ship roll reduction in random waves. Ocean Engineering 37(7), 529–538 (2010)CrossRefGoogle Scholar
  9. 9.
    Hassan, G., Fatemeh, D., Parviz, G., Babak, O.: Neural network-PID controller for roll fin stabilizer. Polish Maritime Research 17(2), 23–28 (2010)Google Scholar
  10. 10.
    Zhang, Y., Shi, W., Yin, L.: Adaptive backstepping and sliding model control of fin stabilizer based on the RBF neural network. Proceedings-Electric Power Applications 149(3), 184–194 (2002)CrossRefGoogle Scholar
  11. 11.
    Ge, S.S., Hang, C.C., Zhang, T.: Stable adaptive neural network control. Kluwer Academic Publishers (2002)Google Scholar
  12. 12.
    Qu, Z.: Robust Control of Nonlinear Uncertain Systems. Wiley, New York (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Weiwei Bai
    • 1
  • Tieshan Li
    • 1
  • Xiaori Gao
    • 1
  • Khin Thuzar Myint
    • 1
  1. 1.Navigation CollegeDalian Maritime UniversityDalianChina

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