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Application-Oriented Adaptive Neural Networks Design for Ship’s Linear-Tracking Control

  • Wei Li
  • Jun Ning
  • Zhengjiang Liu
Conference paper
  • 3.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7952)

Abstract

By employing Radial Basis Function (RBF) Neural Networks (NN) to approximate uncertain functions, an application-oriented adaptive neural networks design for ship linear-tracking control was brought in based on dynamic surface control (DSC) and minimal-learning-parameter (MLP) algorithm. With less learning parameters and reduced computation load, the proposed algorithm can avoid the possible controller singularity problem and the trouble caused by ”explosion of complexity” in traditional backstepping methods is removed, so it is convenient to be implemented in applications. In addition, the boundedness stability of the closed-loop system is guaranteed and the tracking error can be made arbitrarily small. Simulation results on ocean-going training ship ’YULONG’ are shown to validate the effectiveness and the performance of the proposed algorithm.

Keywords

RBF Neural Networks DSC MLP Linear-Tracking Control Backstepping 

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References

  1. 1.
    Pettersen, K.Y., Lefeber, E.: Way-point tracking control of ships. In: Proc. of 40th IEEE CDC, Orlando, USA, pp. 940–945 (December 2001)Google Scholar
  2. 2.
    Ghommam, J., Mnif, F., Benali, A., Derbel, N.: Nonsingular Serret-Frenet based path following control for an underactuated surface vessel. Journal of Dynamic Systems, Measurement and Control 131(2), 1–8 (2009)CrossRefGoogle Scholar
  3. 3.
    Lin, Y., Chen, X., Zhou, T.: Nonlinear PID Design for Ship Course Autopilot Control. Marine Electric & Electronic Technology 29(6), 46–48 (2009)MathSciNetGoogle Scholar
  4. 4.
    Xu, J.H., Liu, Y.J.: The application of Hybrid intelligence system for ship-tracking control, pp. 53–61. Master dissertation of Shanghai Maritime University (2006)Google Scholar
  5. 5.
    Li, T.S., Yu, B., Hong, B.G.: A Novel Adaptive Fuzzy Design for Path Following for Underactuated Ships with Actuator Dynamics. In: ICIEA 2009, pp. 2796–2800 (2009)Google Scholar
  6. 6.
    Yang, Y.S., Ren, J.S.: Adaptive fuzzy robust tracking controller design via small gain approach and its application. IEEE Trans. Fuzzy Syst. 11(6), 783–795 (2003)CrossRefGoogle Scholar
  7. 7.
    Yang, Y.S., Feng, G., Ren, J.S.: A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems. IEEE Trans. Syst. Mon. Cyhern. A. Syst, Humans 34(3), 406–420 (2004)CrossRefGoogle Scholar
  8. 8.
    Wang, D., Hang, J.: Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Trans. Neural Netw. 16(1), 195–202 (2005)CrossRefGoogle Scholar
  9. 9.
    Li, W., Ning, J., Liu, Z., Li, T.: Adaptive Neural Networks Control on Ship’s Linear-Path Following. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part V. LNCS, vol. 7667, pp. 418–427. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wei Li
    • 1
  • Jun Ning
    • 1
  • Zhengjiang Liu
    • 1
  1. 1.Navigation CollegeDalian Maritime UniversityChina

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