Type-2 Fuzzy Logic Controllers Based Genetic Algorithm for the Position Control of DC Motor

Mohammed Zeki  Al-Faiz; Mohammed S.  Saleh; Ahmed A.  Oglah

doi:10.4236/ica.2013.41015

Mohammed Zeki Al-Faiz, Mohammed S. Saleh, Ahmed A. Oglah

Abstract: Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of this article lies in the application of a genetic algorithm interval type-2 fuzzy logic controller (GAIT2FLC) in the design of fuzzy controller for the position control of DC Motor. The entire system has been modeled using MATLAB R11a. The performance of the proposed GAIT2FLC is compared with that of its corresponding conventional genetic algorithm type-1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. Extensive simulation studies are conducted to compare the response of the given system with the conventional genetic algorithm type-1 fuzzy controller to the response given with the proposed GAIT2FLC scheme.

Keywords: Type-2 Fuzzy Logic Controller; Genetic Algorithm; DC Motor

Cite this paper: M. Al-Faiz, M. Saleh and A. Oglah, "Type-2 Fuzzy Logic Controllers Based Genetic Algorithm for the Position Control of DC Motor," Intelligent Control and Automation, Vol. 4 No. 1, 2013, pp. 108-113. doi: 10.4236/ica.2013.41015.

References

[1] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, No. 3, 1965, pp. 338-353. doi:10.1016/S0019-9958(65)90241-X

[2] J. John and R. Langari, “Fuzzy Logic Intelligence, Control and Information,” Prentic Hall, Englewood Cliffs, 1998.

[3] P. King and E. Mamdani, “The Application of Fuzzy Control to Industrial Process,” Automatica, Vol. 13, No. 3, 1977, pp. 235-242. doi:10.1016/0005-1098(77)90050-4

[4] J. M. Mendel, “Rule-Based Fuzzy Logic Systems: Introduction and New Directions,” Prentice-Hall, Englewood Cliffs, 2001.

[5] Q. Liang and J. M. Mendel, “Interval Type-2 Fuzzy Logic Systems: Theory and Design,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 5, 2000, pp. 535-550. doi:10.1109/91.873577

[6] Q. Liang and J. M. Mendel, “MPEG VBR Video Traffic Modelling and Classification Using Fuzzy Technique,” IEEE Transactions on Fuzzy Systems, Vol. 9, No. 1, 2001, pp. 183-193. doi:10.1109/91.917124

[7] J. H. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan Press, Ann Arbor, 1975.

[8] M. Sakawa, “Genetic Algorithms and Fuzzy M Ultiobjective Optimization,” Kluwer Academic, Boston, 1975.

[9] A. Homaifar and E. McCormick, “Simultaneous Design of Membership Functions and Rule Sets for Fuzzy Controllers Using Genetic Algorithms,” IEEE Transactions on Fuzzy Systems, Vol. 3, No. 2, 1995, pp. 129-138. doi:10.1109/91.388168

[10] N. Tutkun and D. Maden, “Determination of the PI Controller Parameters for a DC Shunt Motor Using Genetic Algorithm Method,” International Conference of Electronics and Computer Engineering, Bursa, 2-5 December 2010, pp. 330-333.

[11] X. Du and H. Ying, “Derivation and Analysis of the Structures of the Interval Type-2 Fuzzy-PI and PD Controllers,” IEEE Transactions on Fuzzy Systems, Vol. 18, No. 4, 2010, pp. 802-814. doi:10.1109/TFUZZ.2010.2049022