@inproceedings{1a39ad26a3514699988aebb8bf3db07a,
title = "Fuzzy-like adaptive position control of induction motor",
abstract = "A novel technique named Fuzzy-Like Adaptive Control (FLAC) is proposed for adaptive position control of a nonlinear dynamic plant such as the Induction Motor (IM). To accurately track the rotor position of an IM, the key has been a proper design of a controller. The proposed method utilizes the Modified Gaussian Radial Basis Function Neural Network (M-GRBFNN) for the learning of error. This scheme involves edge triggered standard deviation to update the centers and variances of the error. The major benefit of the proposed approach is ease along with excellent performance. It has been proved that the tracking error converges to the neighborhood of zero. The success of the proposed scheme is confirmed with the help of real-time experiments.",
keywords = "Adaptive tracking, Gaussian radial basis function, Induction motor, Position control, neural network",
author = "Qureshi, {M. Bilal} and Laiq Khan and Shahid Qamar and Muhammad Shafiq",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016 ; Conference date: 14-10-2016 Through 15-10-2016",
year = "2017",
month = mar,
day = "1",
doi = "10.1109/CT-IETA.2016.7868254",
language = "English",
series = "2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Tarek Sobh and Khaled Elleithy",
booktitle = "2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016",
}