Document Type : Review Article

Author

Razi university, Kermansha, Iran

Abstract

The purpose of this paper is to design an adaptive neuro-fuzzy controller to control the speed of the BLDC motor. This paper throws overall view of the performance of fuzzy PID controller and compares with fuzzy - adaptive neural controller. Take characteristics of suitable in PID controller is difficult. But fuzzy is the ability to take appropriate control parameters and calculations easier. An adaptive neuro fuzzy control system has the advantages of both types of fuzzy control system and neuro system. Paper examines the BLDC motor speed control based on adaptive neuro - fuzzy First tried to have a simulated fuzzy PID controller And then designing controller adaptive neuro - fuzzy using ANFIS toolbox for the motor. And we compare the characteristics of speed, torque, current and voltage in the three controllers finally, we conclude that the characteristic of the controller adaptive neuro - fuzzy (ANFIS) is better than the other two controllers.

Keywords

[1] Yen- Shin Lai, Yong- Kai Lin, “A unified approach to zero-crossing point detection of back EMF for brushless DC motor drives without current and hall sensors ”,IEEE Trans. Power Electron. 26 (6) (2011) 1704–1713.
[2] S.A.KH. Mozaffari Niapour, S. Danyali, M.B.B. Sharifian, M.R. Feyzi, “Brushless DC motor drives supplied by PV power system based on Z-source inverter and FL-IC MPPT controller”, Energy Convers. Manage. 52 (8–9) (2011) 3043–3059.
[3] Xiaonan Xin, Hexiu Xu, Yangrui Luo, Huijuan Zhang “A New Method of Brushless DC Motor Control System Simulation” IEEE Trans. Power Deliv. pp. 3667-3670, 2011.
[4] SHI Hao, PAN Zaiping. “The Fuzzy Control System and Simulation of the Brushless DC Motor [J]’’. Micromotors, 2005, 38 (5):42~44.
[5] CHEN Ya-bing, ZHOU Zhi-ping, JI Zhi-cheng. “Study on Fuzzy PI Intelligent Control Strategy of Brushless DC Motor [J]”. Journal of Jiangnan University: Natural Science Edition, 2005, 4 (1):14~18.
[6] WU Xuemei, JING Zhanrong, SHI Yongqi. “Study of Control Technology for Brushless DC Electromotor Based on DSP [J]”. Machinery & Electronics, 2005 (3):50~52.
[7] K. Ang, G. Chong, and Y. Li, “PID control system analysis, design and technology,” IEEE Trans. Control System Technology, vol. 13, pp. 559-576, July 2005.
[8] R. Krishnan, “Electric Motor Drives – Modeling, Analysis, and Control”, Prentice- Hall, Upper Saddle River, 2001.
[9] T.J.E. Miller, “Brushless Permanent-Magnet and Reluctance Motor Drives”, Clarendon Press/Oxford Science Publications, 1989.
[10] Atef Saleh Othman Al-Mashakbeh,“ Proportional Integral and Derivative Control of Brushless DC Motor”, European Journal of Scientific Research 26-28 July 2009, vol. 35, pg 198-203.
[11] J.S.R. Jang, “ANFIS: “Adaptive network- based fuzzy inference system”, IEEE Trans. Sys. Man. Cyb.,Vol. 23,No.3, pp.665-685, May/JUNE 1993.