Fuzzy Adaptive Control Design of Induction Motor Speed Sensorless Based on MRAS

Sheng Ou

School of Electronic and Electrical Engneering, Shanghai University of Engineering Science, Shanghai, 201620, China.

Haishan Liu *

School of Electronic and Electrical Engneering, Shanghai University of Engineering Science, Shanghai, 201620, China.

Qun Peng

School of Electronic and Electrical Engneering, Shanghai University of Engineering Science, Shanghai, 201620, China.

Shiyang Qi

School of Electronic and Electrical Engneering, Shanghai University of Engineering Science, Shanghai, 201620, China.

Cong Feng

School of Electronic and Electrical Engneering, Shanghai University of Engineering Science, Shanghai, 201620, China.

Pinlong Mo

School of Electronic and Electrical Engneering, Shanghai University of Engineering Science, Shanghai, 201620, China.

Guoying Liu

Shanghai Qirod Technology Company, Shanghai, 201700, China.

*Author to whom correspondence should be addressed.


Abstract

In order to solve the real-time parameter adjustment problem of the speed sensorless vector control system for the induction motor, the paper presents a fuzzy self-adaptive method with intelligent gain adjustment. By the means of the sensorless vector control for induction motor, adapt Model reference adaptive system (MRAS) to achieve the rotor position estimation to improve the speed and estimated accuracy, and then design the out-loop controller to achieve high response of speed control. So applying the method of the fuzzy self-adaptive parameter adjustment, track and correct controller parameters in time to achieve high dynamic response. The simulation results confirm the validity and effectiveness of the proposed control strategy.

Keywords: Induction motor, speed-sensorless, fuzzy self-adaptive, model reference adaptive system (MRAS)


How to Cite

Ou, Sheng, Haishan Liu, Qun Peng, Shiyang Qi, Cong Feng, Pinlong Mo, and Guoying Liu. 2015. “Fuzzy Adaptive Control Design of Induction Motor Speed Sensorless Based on MRAS”. Current Journal of Applied Science and Technology 12 (2):1-8. https://doi.org/10.9734/BJAST/2016/19893.

Downloads

Download data is not yet available.