Neuro Fuzzy and Self Tunging Fuzzy Controller to Improve Pitch and Yaw Control Systems Resposes of Twin Rotor MIMO System

Authors: Thair Sh. Mahmoud, Tang Sai Hong, Mohammed H. Marhaban

Abstract:

In this paper, Neuro-Fuzzy based Fuzzy Subtractive Clustering Method (FSCM) and Self Tuning Fuzzy PD-like Controller (STFPDC) were used to solve non-linearity and trajectory problems of pitch AND yaw angles of Twin Rotor MIMO system (TRMS). The control objective is to make the beams of TRMS reach a desired position quickly and accurately. The proposed method could achieve control objectives with simpler controller. To simplify the complexity of STFPDC, ANFIS based FSCM was used to simplify the controller and improve the response. The proposed controllers could achieve satisfactory objectives under different input signals. Simulation results under MATLAB/Simulink® proved the improvement of response and superiority of simplified STFPDC on Fuzzy Logic Controller (FLC).

Keywords: Fuzzy Subtractive Clustering Method, Neuro Fuzzy, Self Tuning Fuzzy Controller, and Twin Rotor MIMO System.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1070157

References:

[1] Manual, Twin Rotor MIMO System Manual. UK Feedback Instruments Ltd., 1998.
[2] B. U. Islam, N. Ahmed, D. L. Bhatti, and S. Khan, "Controller design using fuzzy logic for a twin rotor MIMO system," 2003, pp. 264-268.
[3] Ch. Sh. Liu, L. R. Chen, B. Z. Li, Sh. K. Chen, Z. S. Zeng, " Improvement of the Twin Rotor MIMO System Tracking and Transient Response Using Fuzzy Control Technology", 2006, pp 1-6.
[4] J. G. Juang. W. K. Liu, Ch. Y. Tsai, "Intelligent Control Scheme for Twin Rotor MIMO System", Taipei, Taiwan, 2005, pp. 102-107.
[5] F. M. Adebrez, M. S. Alam, M. O. Tokhi, "Hybrid Control Scheme for Tracking Performance of a Flexible system", Springer Berlin Heidelberg, 2006, pp. 543-550.
[6] A. Rahideh, M. H. Shaheed, "Hybrid fuzzy-PID-based Control of a Twin Rotor MIMO System", 2006, pp. 49-54.
[7] J. G. Juang, K. T. Tu, W. K. Liu, "Hybrid Intelligent PID Control for MIMO System", Springer-Verlag, Berlin Heidelberg, 2006, pp. 654-663.
[8] J. G. Juang, K. T. Tu, W. K. Liu, "Comparison of classical control and intelligent control for a MIMO system", Applied Mathematics and Computation, Elsevier Inc., 2008.
[9] J. G. Juang, W. K. Liu, "Fuzzy Compensator Using RGA for TRMS Control", Springer-Verlag, Berlin Heidelberg, 2006, pp. 120-126.
[10] J. G. Juang, K. T. Tu, W. K. Liu, "Optimal Fuzzy Sweitching Grey Prediction with RGA for TRMS Control", Taipei, Taiwan, 2006 pp. 681- 686..
[11] H. Zhang and D. Liu, Fuzzy Modeling and Fuzzy Control: Control Engineering, Brikha├╝ser, Boston, 2006.
[12] S. Chopra, R. Matira, V. Kumar, "Analysis of Fuzzy PI and PD Type Controllers Using Subtractive Clustering" International Journal of Computational Cognition, 2006, Vol. 4, No. 2.
[13] S. Chopra, R. Matira, V. Kumar, "Neural Network Tuned Fuzzy Controller for MIMO System", International Journal of Intelligent Technology, 2007, Vol. 2, No. 1.
[14] D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Springer-Verlag, New York, 1993.
[15] R. K. Mudi and N. R. Pal, "A self tuning PI controller", Journal of Fuzzy sets and systems, Elsevier Science, 2000.
[16] Getting Started, ANFIS and the ANFIS GUI, MathWorks Inc., http://www.mathworks.com, accessed on 24 September 2008.
[17] S. L. Chiu, "Fuzzy models identification based on cluster estimation", Journal of Intelligent and Fuzzy Systems, John Wiley & Sons, 1994, Vol. 2, pp. 267-278.