JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Improved Adaptive Neural Network Autopilot for Track-keeping Control of Ships: Design and Simulation
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Improved Adaptive Neural Network Autopilot for Track-keeping Control of Ships: Design and Simulation
Nguyen, Phung-Hung; Jung, Yun-Chul;
  PDF(new window)
 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.
 Keywords
Adaptive neural network;Autopilot;Track-keeping;Ship control;Track-keeping simulation;
 Language
English
 Cited by
1.
Automatic Berthing Control of Ship Using Adaptive Neural Networks,;;

한국항해항만학회지, 2007. vol.31. 7, pp.563-568 crossref(new window)
 References
1.
Brandt, R. D. and Lin, F. (1999), 'Adaptive interaction and its application to neural networks', Elsevier, Information Science 121 (pp. 201-215) crossref(new window)

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 crossref(new window)

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 crossref(new window)

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 crossref(new window)

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