Untitled Document
   
You are from : ( )  
     
Untitled Document
Untitled Document
 

International Journal of Information Technology & Computer Science ( IJITCS )

Abstract :

This paper presents the utilization of backpropagation neural network as a direct inverse control system for a double propeller boat model. Fine tuning method is then conducted by re-learning of the neural controller through the error between the inversed-plant output and control signal of the plant. Then, the proposed controller systems are tested using an open-loop direct inverse control scheme with, and without, feedback signal from the plant output. Simulations analysis showed that the proposed controller could control the boat with high accuracy. It was revealed also from these experiments, that the open-loop scheme without output feedback signal produced lower error compare with that of open-loop scheme with an output feedback signal .

Keywords :

: Boat control system; neural network controller, backpropagation learning, direct inverse controller, fine tuning method.

References :

  1. T. I. Fossen, Marine Control Systems: Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles. Trondheim, Norway: Marine Cybernetics, 2002, ch. 3, pp. 9-12, 49-75.
  2. S.D.Lee, C.-Y.Tzeng, Y.Z.Kehr, C.C.Huang, C.K.Kang, “Autopilot System Based on Color Recognition Algorithm and Internal Model Control Scheme for Controlling Approaching Maneuvers of a Small Boat”, IEEE Journal of Oceanic Eng., vol. 35, no. 2, pp. 376387, April 2010.
  3. C.-Y. Tzeng, "An internal model control approach to the design of yaw-rate-control ship-steering autopilot," IEEE Journal of Oceanic Eng., vol.24, no.4, pp. 507-513, Oct 1999.
  4. R. S. Burns, “The use of artificial neural networks for the intelligent optimal control of surface ships,” IEEE Journal of Oceanic Eng., vol. 20, no. 1, pp. 65–72, Jan 1995.
  5. R. Richter and R. S. Burns, "An artificial neural network autopilot for small vessels", UKSS 93, 1993.
  6. A. Leonessa, T. VanZwieten, and Y. Morel, “Neural network model reference adaptive control of marine vehicles,” in Current Trends in Nonlinear Systems and Control. Boston, MA: Birkhäuser, 2006, pt. IV, pp. 421– 440.
  7. Y. Yang, C. Zhou, and J. Ren, “Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems,” Appl. Soft Comput., vol. 3, pp. 305–316, 2003.
  8. K. P. Tee and S. S. Ge, “Control of fully actuated ocean surface vessels using a class of feedforward approximators,” IEEE Trans. on Control Systems and Technology, vol. 14, no. 4, pp. 750–756, Jul. 2006.
  9. T. I. Fossen and Å. Grøvlen, “Nonlinear Output Feedback Control of Dynamically Positioned Ships Using Vectorial Observer Backstepping,” IEEE Trans. Control Syst. Technol., vol. 6, no. 1, pp. 121–128, Jan. 1998.
  10. S.-L. Dai, C. Wang, F. Luo, "Identification and learning control of ocean surface ship using neural networks", IEEE Trans. on Industrial Informatics, vol. 8, no. 4, pp. 801-810, 2012.
  11. T. Fossen. Guidance and Control of Ocean Vehicles. Wiley, New York, 1994.
  12. K. R. Muske, H. Ashrafiuon, “Identification of a control oriented nonlinear dynamic USV model”, Proc. of 2008 American Control Conference, Washington, USA, June 2008.
  13. P. J. Werbos, ”Neural networks for control and system identification”, Proc. IEEE Conf. Decision and Control, 1989.
  14. K. S. Narendra and K. Parthasaraty, “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, vol.1, pp 4-27, 1990.

Untitled Document
     
Untitled Document
   
  Copyright © 2014  IJITCS.  All rights reserved. IISRC® is a registered trademark of IJITCS Properties.