Skip to main content
Log in

Robust adaptive NN-based output feedback control for a dynamic positioning ship using DSC approach

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

A robust adaptive NN-based output feedback control scheme is presented for a dynamic positioning ship with uncertainties and unknown external disturbances. We tackle the problem that velocity vector of a ship is not available by employing a high-gain observer, and develop the proposed control approach by combing vectorial backstepping with dynamic surface control approach, which is simpler and easier to implement in engineering practice. The neural network (NN) approximation technique is used to compensate for the uncertainties and unknown external disturbances, and it removes the requirement for the prior knowledge about the vessel parameters and external disturbances. Also, it is demonstrated that the proposed control strategy can force the position and yaw angle of a dynamic positioning ship to approach the desired point while guaranteeing all singles of the designed closed-loop dynamic positioning system semi-globally uniformly ultimately bounded by means of the Lyapunov function. Simulation results of a supply ship illustrate the effectiveness of the proposed scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Sϕrensen A J. A survey of dynamic positioning control systems. Annu Rev Control, 2011, 35: 123–136

    Article  Google Scholar 

  2. Fossen T I. Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles. Throndheim: Marine Cybernetics AS, 2002

    Google Scholar 

  3. Balchen J G, Jenssen N A, Salid S. Dynamic positioning using Kalman filtering and optimal control theory. In: Proceedings of IFAC/IFIP Symposium on Automation in Offshore Oil Field Operation, Bergen, 1976. 183–186

    Google Scholar 

  4. Balchen J G, Jenssen N A, Mathisen E, et al. Dynamic positioning system based on Kalman filtering and optimal control. Model Ident Control, 1980, 1: 135–163

    Article  Google Scholar 

  5. Fung P T, Grimble M J. Dynamic ship positioning using a self-tuning Kalman filter. IEEE Trans Automat Control, 1983, 28: 339–350

    Article  MATH  Google Scholar 

  6. Fossen T I, Grϕvlen A. Nonlinear output feedback control of dynamically positioned ships using vectorial observer backstepping. IEEE Trans Control Syst T, 1998, 6: 121–128

    Article  Google Scholar 

  7. Du J L, Li WH, Zheng K, et al. Nonlinear output feedback control of dynamic positioning system of ships (in Chinese). J South China Univ, 2012, 40: 70–75

    Google Scholar 

  8. Fossen T I, Strand J P. Passive nonlinear observer design for ships using Lyapunov methods: full-scale experiments with a supply vessel. Automatica, 1999, 35: 3–16

    Article  MATH  MathSciNet  Google Scholar 

  9. Loria A, Fossen T I. A separation principle for dynamic positioning of ships: theoretical and experimental results. IEEE Trans Control Syst T, 2000, 8: 332–343

    Article  Google Scholar 

  10. Hassani V, Sϕrensen A J, Pascoal A M, et al. Multiple model adaptive wave filtering for dynamic positioning of marine vessels. In: Proceedings of 2012 American Control Conference, Montréal, 2012. 6222–6228

    Chapter  Google Scholar 

  11. Do K D. Global robust and adaptive output feedback dynamic positioning of surface ships. J Mar Sci Appl, 2011, 10: 325–332

    Article  Google Scholar 

  12. Esfandiari F, Khalil H K. Observer-based design of uncertain systems: recovering state feedback robustness under matching conditions. In: Proceedings of Allerton Annual Conference on Communication, Control and Computing, Monticello, 1987. 97–106

    Google Scholar 

  13. Ge S S, Zhang J. Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback. IEEE Trans Neural Network, 2003, 14: 900–918

    Article  Google Scholar 

  14. Khalil H K. High-Gain Observers in Nonlinear Feedback Control. In: Nijmeijer H, Fossen T I. New Directions in Nonlinear Observer Design. Volume 244 of Lecture Notes in Control and Information Sciences. London: Springer, 1999. 249–268

    Chapter  Google Scholar 

  15. Polycarpou M M. Stable adaptive neural control scheme for nonlinear systems. IEEE Trans Automat Control, 1996, 41: 447–451

    Article  MATH  MathSciNet  Google Scholar 

  16. Polycarpou M M, Mears M J. Stable adaptive tracking of uncertain systems using nonlinearly parameterized on-line approximators. Int J Control, 1998, 70: 363–384

    Article  MATH  MathSciNet  Google Scholar 

  17. Zhang T, Ge S S, Hang C C. Adaptive neural network control for strict-feedback nonlinear systems using backstepping design. Automatica, 2000, 36: 1835–1846

    Article  MATH  MathSciNet  Google Scholar 

  18. Ge S S, Wang C. Adaptive NN control of uncertain nonlinear pure-feedback systems. Automatica, 2002, 38: 671–682

    Article  MATH  MathSciNet  Google Scholar 

  19. Wang D, Huang J. Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form. Automatica, 2002, 38: 1365–1372

    Article  MATH  Google Scholar 

  20. Swaroop D, Hedrick J K, Yip P P, et al. Dynamic surface control for a class of nonlinear systems. IEEE Trans Automat Control, 2000, 45: 1893–1899

    Article  MATH  MathSciNet  Google Scholar 

  21. Wang D, Huang J. Neural network based adaptive dynamic surface control for nonlinear systems in strict-feedback form. IEEE Trans Neural Network, 2005, 16: 195–202

    Article  Google Scholar 

  22. Zhang T P, Ge S S. Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form. Automatica, 2008, 44: 1895–1903

    Article  MATH  MathSciNet  Google Scholar 

  23. Wang D. Neural network-based adaptive dynamic surface control of uncertain nonlinear pure-feedback systems. Int J Robust Nonlin, 2011, 21: 527–541

    Article  MATH  Google Scholar 

  24. Li T S, Wang D, Feng G, et al. A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems. IEEE Trans Syst Man Cy B, 2010, 40: 915–927

    Article  Google Scholar 

  25. Ge S S, Hang C C, Lee T H, et al. Stable Adaptive Neural Network Control. Berlin: Springer Publishing Company, Incorporated, 2010

    Google Scholar 

  26. Tee K P, Ge S S. Control of fully actuated ocean surface vessels using a class of feedforward approximators. IEEE Trans Control Syst T, 2006, 14: 750–756

    Article  Google Scholar 

  27. Kurdila A J, Narcowich F J, Ward J D. Persistency of excitation in identification using radial basis function approximants. SIAM J Control Optim, 1995, 33: 625–642

    Article  MATH  MathSciNet  Google Scholar 

  28. Behtash S. Robust output tracking for non-linear systems. Int J Control, 1990, 51: 1381–1407

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yang Yang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Guo, C. & Du, J. Robust adaptive NN-based output feedback control for a dynamic positioning ship using DSC approach. Sci. China Inf. Sci. 57, 1–13 (2014). https://doi.org/10.1007/s11432-014-5127-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-014-5127-3

Keywords

Navigation