An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive

M. A. Rahman; M. N. Uddin; M. A. Abido

doi:10.1109/PCC.2002.998539

An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive

M. A. Rahman
, M. N. Uddin
, M. A. Abido

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

13 Scopus citations

Abstract

An artificial neural network (ANN) for online tuning of a genetic algorithm based PI controller for interior permanent magnet synchronous motor (IPMSM) drive is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating various uncertainties. At each operating condition a genetic algorithm (GA) is used to optimize proportional-integral (PI) controller parameters in a closed loop vector control scheme. In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A radial basis function network (RBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed controller is successfully implemented in real-time using a digital signal processor board DS1102 for a laboratory 1 hp IPMSM. The efficacy of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed approach is found to be a robust controller for application in the IPMSM drive.

Original language	English
Title of host publication	Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	154-160
Number of pages	7
ISBN (Electronic)	0780371569, 9780780371569
DOIs	https://doi.org/10.1109/PCC.2002.998539
State	Published - 2002

Publication series

Name	Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002
Volume	1

Bibliographical note

Publisher Copyright:
© 2002 IEEE.

Keywords

Interior permanent magnet motor
PI controller
artificial neural network
digital signal processor
genetic algorithm
vector control

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/PCC.2002.998539

Cite this

Rahman, M. A., Uddin, M. N., & Abido, M. A. (2002). An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive. In Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002 (pp. 154-160). Article 998539 (Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PCC.2002.998539

Rahman, M. A. ; Uddin, M. N. ; Abido, M. A. / An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive. Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002. Institute of Electrical and Electronics Engineers Inc., 2002. pp. 154-160 (Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002).

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title = "An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive",

abstract = "An artificial neural network (ANN) for online tuning of a genetic algorithm based PI controller for interior permanent magnet synchronous motor (IPMSM) drive is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating various uncertainties. At each operating condition a genetic algorithm (GA) is used to optimize proportional-integral (PI) controller parameters in a closed loop vector control scheme. In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A radial basis function network (RBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed controller is successfully implemented in real-time using a digital signal processor board DS1102 for a laboratory 1 hp IPMSM. The efficacy of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed approach is found to be a robust controller for application in the IPMSM drive.",

keywords = "Interior permanent magnet motor, PI controller, artificial neural network, digital signal processor, genetic algorithm, vector control",

author = "Rahman, \{M. A.\} and Uddin, \{M. N.\} and Abido, \{M. A.\}",

note = "Publisher Copyright: {\textcopyright} 2002 IEEE.",

year = "2002",

doi = "10.1109/PCC.2002.998539",

language = "English",

series = "Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002",

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Rahman, MA, Uddin, MN & Abido, MA 2002, An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive. in Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002., 998539, Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002, vol. 1, Institute of Electrical and Electronics Engineers Inc., pp. 154-160. https://doi.org/10.1109/PCC.2002.998539

An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive. / Rahman, M. A.; Uddin, M. N.; Abido, M. A.
Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002. Institute of Electrical and Electronics Engineers Inc., 2002. p. 154-160 998539 (Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002; Vol. 1).

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

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AU - Abido, M. A.

PY - 2002

Y1 - 2002

N2 - An artificial neural network (ANN) for online tuning of a genetic algorithm based PI controller for interior permanent magnet synchronous motor (IPMSM) drive is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating various uncertainties. At each operating condition a genetic algorithm (GA) is used to optimize proportional-integral (PI) controller parameters in a closed loop vector control scheme. In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A radial basis function network (RBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed controller is successfully implemented in real-time using a digital signal processor board DS1102 for a laboratory 1 hp IPMSM. The efficacy of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed approach is found to be a robust controller for application in the IPMSM drive.

AB - An artificial neural network (ANN) for online tuning of a genetic algorithm based PI controller for interior permanent magnet synchronous motor (IPMSM) drive is presented in this paper. The proposed controller is developed for accurate speed control of the IPMSM drive under system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating various uncertainties. At each operating condition a genetic algorithm (GA) is used to optimize proportional-integral (PI) controller parameters in a closed loop vector control scheme. In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A radial basis function network (RBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed controller is successfully implemented in real-time using a digital signal processor board DS1102 for a laboratory 1 hp IPMSM. The efficacy of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed approach is found to be a robust controller for application in the IPMSM drive.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Rahman MA, Uddin MN, Abido MA. An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive. In Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002. Institute of Electrical and Electronics Engineers Inc. 2002. p. 154-160. 998539. (Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002). doi: 10.1109/PCC.2002.998539

An artificial neural network for online tuning of genetic algorithm based PI controller for interior permanent magnet synchronous motor drive

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this