Cover
Vol. 8 No. 1 (2012)

Published: November 30, 2012

Pages: 25-34

Original Article

A Self Learning Fuzzy Logic Controller for Ship Steering System

Ammar A. Aldair 1
1Electrical Engineering Department Engineering College University of Basrah, Iraq
History
  • Received: September 30, 2012
  • Available Online: November 30, 2012
DOI

https://doi.org/10.37917/ijeee.8.1.3

Abstract

A self learning fuzzy logic controller for ship steering systems is proposed in this paper. Due to the high nonlinearity of ship steering system, the performances of traditional control algorithms are not satisfactory in fact. An intelligent control system is designed for controlling the direction heading of ships to improve the high e ffi ciency of transportation, the convenience of manoeuvring ships, and the safety of navigation. The design of fuzzy controllers is usually performed in an ad hoc manner where it is hard to justify the choice of some fuzzy control parameters such as the parameters of membership function. In this paper, self tuning algorithm is used to adjust the parameters of fuzzy controller. Simulation results show that the efficiency of proposed algorithm to design a fuzzy controller for ship steering system.

Keywords

References

  1. J. R. Layne and K. M. Passino, “Fuzzy Model Reference Learning Control for Cargo Ship Steering,” IEEE Control Systems Magazine, vol. 13, no. 6, pp. 23–34, 1993.
  2. K. Seo et al., “Ontology-based Fuzzy Support Agent for Ship Steering Control,” Expert Systems with Applications, vol. 36, pp. 755–765, 2009.
  3. J. Cheng, J. Yi, and D. Zhao, “Neural Network Based Model Reference Adaptive Control for Ship Steering System,” International Journal of Information Technology, vol. 11, no. 6, pp. 75–82, 2005.
  4. K. Seo et al., “Intelligent Steering Control System Based on Voice Instructions,” International Journal of Control, Automation and Systems, vol. 5, no. 5, pp. 539–546, 2007.
  5. Y. Yang, C. Zhou, and J. Ren, “Model Reference Adaptive Robust Fuzzy Control for Ship Steering Autopilot with Uncertain Nonlinear Systems,” Applied Soft Computing, vol. 3, pp. 305–316, 2003.
  6. H. R. Berenji and P. Khedkar, “Learning and Tuning Fuzzy Logic Controllers Through Reinforcements,” IEEE Transactions on Neural Networks, vol. 3, pp. 724–740, 1992.
  7. R. Jang, “Self Learning Fuzzy Controller Based on Temporal Back Propagation,” IEEE Transactions on Neural Networks, vol. 3, pp. 714–723, 1992.
  8. P. J. King, K. J. Burnham, and D. J. G. James, “A Model Reference Self Organizing Fuzzy Logic Controller,” Control and Computers, vol. 23, no. 1, 1995.
  9. S. Bogdan and Z. Kovacic, “On the Design of Self Learning Fuzzy Controllers for Nonlinear Control Systems by Using a Reference Model and Sensitivity,” in Proc. 4th IEEE Mediterranean Symposium on Control and Automation, Chania, Greece, 1996.
  10. C. Chiang, H. Chung, and J. Lin, “A Self-Learning Fuzzy Logic Controller Using Genetic Algorithms with Reinforcements,” IEEE Transactions on Fuzzy Systems, vol. 5, no. 3, pp. 460–467, 1997.
  11. M. A. Lee and H. Takagi, “Integrating Design Stages of Fuzzy Systems using Genetic Algorithms,” in Proc. 2nd International Conference on Fuzzy Systems, 1993.
  12. Z. Kovacic, S. Bogdan, and M. Balenovic, “A Model Reference and Sensitivity Model-Based Self-Learning Fuzzy Logic Controller as a Solution for Control of Nonlinear Servo Systems,” IEEE Transactions on Energy Conversion, vol. 14, no. 4, pp. 1479–1484, 1999.
  13. Z. Kovacic, M. Balenovic, and S. Bogdan, “Sensitivity-Based Self-Learning Fuzzy Logic Control for a Servo System,” IEEE Control Systems Magazine, vol. 18, no. 3, pp. 41–51, 1998.
  14. S. Huang and W. Lin, “A Self-Organizing Fuzzy Controller for an Active Suspension System,” Journal of Vibration and Control, vol. 9, pp. 1023–1040, 2003.
  15. M. Bech and L. Smitt, “Analogue Simulation of Ship Maneuvers,” Hydro-og Aerodynamisk Laboratorium, Lyngby, Denmark, 1969.