Cover
Vol. 14 No. 1 (2014)

Published: January 31, 2014

Pages: 58-68

Original Article

DESIGN NEUROFUZZY WITH PID CONTROLLERS FOR AN AUTONOMOUS MINI-HELICOPTER SYSTEM

Ammar A. Aldair 1
1 Electrical Engineering Department, Engineering College, University of Basrah, Iraq
History
  • Received: October 31, 2013
  • Available Online: January 31, 2014
DOI

https://doi.org/10.33971/bjes.14.1.6

Abstract

In this paper a combining Neurofuzzy and PID controllers have been employed for controlling the positions and rotational motions of the mini-helicopter system. Due to the strong coupling between the state variables of the mini-helicopter model, therefore, it is not suitable to design single controller for regulating the positions and rotational motions of the given model. To solve this problem, three neurofuzzy controllers are designed for the lateral, longitudinal and heave motion; and three classical PID controllers are proposed for attitude control. Nine rules are suggested for each neurofuzzy network depends on the previous knowledge/experiences of expert human pilot. The simulation results show that the proposed controllers are very effective to control the hovering, position and forward flight of the mini-helicopter system.

Keywords

References

  1. T. John Koo and S. Sastry. Output Tracking Control Design of a Helicopter Model Based on Approximate Linearization. in Proceeding 37th IEEE Conference on Decision and Control . Tampa, FL USA. 1998.
  2. L. R. Salinas, Emanul Slawinski, and Vicente A. Mut, Kinematic Nonlinear Controller for a Miniature Helicopter Via Lyapunov Techniques. Asian Journal of Control, 2013. Article first published online: Dol 10.1002/asjc.754.
  3. M. M. Zaheri and L. Chen, Intelligent Predictive Control of a Model Helicopter's Yaw Angle. Asian Journal of Control, 12 (6): 2010 p. 667-679..
  4. J. V. R. Prasad, et al. Adaptive Nonlinear Controller Synthesis and Flight Test Evaluation on an Unmanned Helicopter. in Proceedings IEEE International Conference on Control Applications . Hawaii, USA. 1999.
  5. H. Shim. A Comprehensive Study of Control Design for an Autonomous Helicopter . in Proceedings 37th IEEE Conference on Decision and Control. Tampa, Florida, USA. 1998.
  6. T. Yamaguchi, K. Goto, and T. Takagi, Two-degree-of-freedom Fuzzy Model Using Associative Memories and its Applications. Information Sciences, 71 : 1993, p. 65-97.
  7. M. Sugeno, Development of an Intelligent Unmanned Helicopter in Fuzzy Systems and The Second International Fuzzy Engineering Symposium .. Yokohama. 1999 p. 33-39.
  8. E. N. Sanchez, H. M. Becerra, and C.M. Velez, Combining fuzzy PID and Regulation Control for an Autonomous Mini-Helicopter. Information Sciences 177 : 2007, p. 19992022..
  9. C. Phillips, C. L. Karr, and G. Walker, Helicopter Flight Control with Fuzzy Logic Genetic Algorithms. Engineering Applications of Artificial Intelligence 9 (2): 1996, p. 175184.
  10. P. melin and O. Castillo, Intelligent control of aircraft dynamic systems with a new hybrid neuro-fuzzy-fractal approach. Information Sciences, 142 : 2002 p. 161-175.
  11. A. M. Sharhri and Fazel Naghdy, Neurofuzzy Impact Control (NFIC) for Antipersonnel (AP) Mines Detection. Asian Journal of Control, 4 (2): 2002 p. 127-137.
  12. S. M. Yang, Y. J. Tung, and Y.C. Liu, A Neurofuzzy System Design Methodology for Vibration Control Asian Journal of Control, 7 (4): p. 393-400. 2005.
  13. V. Gavrilets, B. Mettler, and E. Feron, Dynamic Model for a miniature aerobatic Helicopter , in MIT-LIDS report . 2003.
  14. C. Isik and M. Farrokhi. Recurrent Neurofuzzy System . in Annual meeting of the North American Fuzzy Information Processing Society Nafips . 1997.
  15. M. Brown and C. Harris, Neurofuzzy Adaptive Modeling and Control . prentice hall international (UK) limited. 1994.
  16. Y. Zhang and A. Kandel, Compensatory Neurofuzzy Systems with Fast Learning Algorithms. IEEE transactions on neural network, 9 (1): 1998, p. 80-105.
  17. R. J. Jang, C. T. Sun, and M. E., Neurofuzzy and soft Computing: A Computational Approach to Learning and Machine Intelligence , ed. U.S.R. Prentice Hall, NJ 07458. 1997, USA.
  18. J. Jang, ANFIS: Adaptive Network Based Fuzzy Inference System. IEEE Transaction on System, Man and Cybernetics 23, 1993, p. 665-686.