Journal of Navigation and Port Research (한국항해항만학회지)

Korean Institute of Navigation and Port Research (한국항해항만학회)
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Improved Adaptive Neural Network Autopilot for Track-keeping Control of Ships: Design and Simulation

  • Published : 2006.06.01
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Abstract

This paper presents an improved adaptive neural network autopilot based on our previous study for track-keeping control of ships. The proposed optimal neural network controller can automatically adapt its learning rate and number of iterations. Firstly, the track-keeping control system of ships is described For the track-keeping control task, a way-point based guidance system is applied To improve the track-keeping ability, the off-track distance caused by external disturbances is considered in learning process of neural network controller. The simulations of track-keeping performance are presented under the influence of sea current and wind as well as measurement noise. The toolbox for track-keeping simulation on Mercator chart is also introduced.

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References

  1. Brandt, R. D. and Lin, F. (1999), 'Adaptive interaction and its application to neural networks', Elsevier, Information Science 121 (pp. 201-215) https://doi.org/10.1016/S0020-0255(99)00090-0
  2. Fossen, T. I. (2002), 'Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles', Marine Cybernetics, Trondheim, Norway. ISBN 82-92356-00-2
  3. Fossen, T. I. (2005), GNC Toolbox for MATLAB (http://www.cesos.ntnu.no/mss/MarineGNC/index.htm. accessed 2005/Dec)
  4. Nguyen, P. H. and Jung, Y. C. (2005), 'An Adaptive Autopilot for Course-keeping and Track-keeping Control of Ships using Adaptive Neural Network (Part I: Theoretical Study)', International Journal of Navigation and Port Research (KINPR), Vol.29, No.9 pp. 771-776, ISSN-1598-5725 https://doi.org/10.5394/KINPR.2005.29.9.771
  5. Nguyen, P. H. and Jung, Y. C. (2006), 'An Adaptive Autopilot for Course-keeping and Track-keeping Control of Ships using Adaptive Neural Network (Part II: Simulation Study)', International Journal of Navigation and Port Research (KINPR), Vol.30, No.2 pp. 119-124, ISSN-1598-5725 https://doi.org/10.5394/KINPR.2006.30.2.119
  6. Rich Powlowicz (2005), M_Maps homepage (http://www2.ocgy.ubc.ca/~rich/map.html,accessed2005/Dec)
  7. Saikalis, G. and Lin, F. (2001), 'A Neural Network Controller by Adaptive Interaction', Proceedings of the American Control Conference, Arlington, pp. 1247-1252
  8. Velagic, J., Vukiz, Z., Omerdic, E. (2003), 'Adaptive Fuzzy Ship Autopilot for Track-keeping', Control Engineering Practice 11, pp. 433-443 https://doi.org/10.1016/S0967-0661(02)00009-6
  9. Vukic, Z., Omerdic, E., Kuljaca, L. (1997), 'Fuzzy Autopilot for Ships Experiencing Shallow Water Effect in Manoeuvring', Manoeuvring and Control of Marine Craft, A proceedings volume from the IFAC Conference, Brijuni, Croatia, pp. 99-104 (Edited by Z. Vukic and G. N. Roberts)
  10. Vukic, Z., Omerdic, E., Kuljaca, L. (1998), 'Improved Fuzzy Autopilot for Track-keeping', Control Applications in Maritime Systems, A proceedings volume from the IFAC Conference, Fukuoka, Japan, pp. 123-128 (Edited by K. Kijima and T. I. Fossen)
  11. Yoon, Y. J. and Jeon, S. H. (2005), 'Terrestrial Navigation' (in Korean), Korea Maritime University
  12. Zhang, Y., Hearn, G. E. and Sen, P. (1997), 'Neural network approaches to a class of ship control problems (Part I & II)', Eleventh Ship Control Systems Symposium Vol. 1 (Edited by P. A. Wilson)
  13. Zhuo, Y. and Hearn, G. E. (2004), 'Specialized Learning for Ship Intelligent Track-keeping Using Neurofuzzy', Proceedings of IFAC-CAMS, Ancona, Italy, pp. 291-296, July, 7-9