[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5207/JIEIE.2012.26.7.009

Adaptive Fuzzy Neuro Controller for Speed Control of Induction Motor

Ko, Jae-Sub (Electrical Control Engineering at Sunchon National University)
Chung, Dong-Hwa (Electrical Control Engineering at Sunchon National University)

Publication Information

Journal of the Korean Institute of Illuminating and Electrical Installation Engineers / v.26, no.7, 2012 , pp. 9-15 More about this Journal

Abstract

This paper is proposed the adaptive fuzzy neuro controller(AFNC) for high performance of induction motor drive. The design of this algorithm based on the AFNC that is implemented using fuzzy controller(FC) and neural network(NN). This controller uses fuzzy rule as training patterns of a NN. Also, this controller adjusts the weights between the neurons of NN to minimize the error between the command output and the actual output using the back-propagation method. The control performance of the AFNC is evaluated by analysis in various operating conditions. The results of analysis prove that the proposed control system has high performance and robustness to parameter variation, and steady-state accuracy and transient response.

Keywords

Induction Motor Drive; Fuzzy Controller; Neural Network; AFNC; High Performance;

Citations & Related Records

Reference

1	L. Chern and Y. C. Wu, "Design of integral variable structure controller and application to electro hydraulic velocity servo systems," IEEE Proc. D Control Theory and Application, vol. 138, pp. 439-444, 1991. DOI ScienceOn
2	K. J. Astrom and T. Hagglund, "Automatic tuning of PID controller," ISA Research Triangle Park, North Carolina, 1988.
3	M. N. Uddin, et al., "Performance of fuzzy logic based indirect vector control for induction motor drive," IEEE Trans. on IA, vol. 38, pp. 1219-1225, 2002.
4	Y. G. Leu, T. T. Lee and W. Y. Wang, "On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems," IEEE Trans. Syst., Man Cybern., vol. 27, pp. 1034-1043, 1997. DOI ScienceOn
5	J. Zhang and A. J. Morris, "Fuzzy neural networks for nonlinear systems modeling," in Proc. Inst. Elect. Eng. Cont. Theory Appl., vol. 142, pp. 551-556, 1995. DOI
6	T. S. R. Jang and C. T. Sun, "Neural-fuzzy modeling and control," Proc. IEEE, vol. 83, pp. 378-405, 1995. DOI ScienceOn
7	I. S. Baruch, I. P. Cruz, "An indirect adaptive neural control of a three phase induction motor velocity," ICEEE Conf, pp. 105-110, 2010.
8	G. Fong and K. J. Chang, "Neural-network-based self-tuning PI controller for precise motion control of PMAC motors," IEEE Trans. on IE, vol. 48, pp. 408-415, 2001.
9	M. N. Uddin, H. Wen, "Development of a self-tuned neuro-fuzzy controller for induction motor drive," IEEE Industry Applications, vol 43, pp. 1108-1116, 2007. DOI
10	B. Sahu, K. B. Mohanty, S. Pati, "A comparative study on fuzzy and PI speed controllers for field-oriented induction motor dirve," IECR Conf, pp. 191-196, 2010.
11	M. Cheng, et al., "New self-tuning fuzzy PI control of a novel doubly salient permanent magnet motor drive," IEEE Trans. on IE, vol. 53, pp. 814-821, 2006.
12	F. Barrero, et al., "Speed control of induction motors using a novel fuzzy sliding-mode structure," IEEE Trans. on Fuzzy Systems, vol. 10, pp. 375-383, 2002. DOI
13	M. Masiala, et al., "Fuzzy self-tuning speed control of an indirect field-oriented control induction motor drive," in 41st annual Meeting of the IEEE IAS, Tampa, FL, pp. 1732-1740, 2006.
14	L. Zhen and L. Xu, "Sensorless field orientation control of induction machines based on a mutual MRAS scheme," IEEE Trans. on IE, vol. 45, pp. 824-831, 1998.
15	W. Chung-Yuen, et al., "An induction motor servo system with improved sliding mode control," in IECI and Automation, 1992 PEMC., Proc. of the 1992 International Conf. on, vol. 1, pp. 60-66, 1992.