Fuzzy-like adaptive position control of induction motor

M. Bilal Qureshi; Laiq Khan; Shahid Qamar; Muhammad Shafiq

doi:10.1109/CT-IETA.2016.7868254

Fuzzy-like adaptive position control of induction motor

M. Bilal Qureshi^*, Laiq Khan, Shahid Qamar, Muhammad Shafiq

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509007998
DOIs	https://doi.org/10.1109/CT-IETA.2016.7868254
Publication status	Published - Mar 1 2017
Event	2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016 - Bridgeport, United States Duration: Oct 14 2016 → Oct 15 2016

Other

Other	2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016
Country/Territory	United States
City	Bridgeport
Period	10/14/16 → 10/15/16

Keywords

Adaptive tracking
Gaussian radial basis function
Induction motor
neural network
Position control

ASJC Scopus subject areas

Control and Systems Engineering
Computational Theory and Mathematics
Artificial Intelligence
Software
Electrical and Electronic Engineering

Access to Document

10.1109/CT-IETA.2016.7868254

Cite this

Fuzzy-like adaptive position control of induction motor. / Qureshi, M. Bilal; Khan, Laiq; Qamar, Shahid et al.
2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7868254.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Qureshi, MB, Khan, L, Qamar, S & Shafiq, M 2017, Fuzzy-like adaptive position control of induction motor. in 2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016., 7868254, Institute of Electrical and Electronics Engineers Inc., 2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016, Bridgeport, United States, 10/14/16. https://doi.org/10.1109/CT-IETA.2016.7868254

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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, neural network, Position control",

author = "Qureshi, {M. Bilal} and Laiq Khan and Shahid Qamar and Muhammad Shafiq",

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AU - Qureshi, M. Bilal

AU - Khan, Laiq

AU - Qamar, Shahid

AU - Shafiq, Muhammad

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Y1 - 2017/3/1

N2 - 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.

AB - 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.

KW - Adaptive tracking

KW - Gaussian radial basis function

KW - Induction motor

KW - neural network

KW - Position control

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BT - 2016 Annual Connecticut Conference on Industrial Electronics, Technology and Automation, CT-IETA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 14 October 2016 through 15 October 2016

ER -

Fuzzy-like adaptive position control of induction motor

Abstract

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Keywords

ASJC Scopus subject areas

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