Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent-Magnet Synchronous Motor Drives

M. Nasir Uddin; M. A. Abido; M. Azizur Rahman

doi:10.1109/TIA.2003.821797

Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent-Magnet Synchronous Motor Drives

M. Nasir Uddin^*, M. A. Abido, M. Azizur Rahman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

124 Scopus citations

Abstract

A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an 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 various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition a genetic algorithm is used to optimize the 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 fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN-based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1-hp IPMSM. The effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications in an IPMSM drive.

Original language	English
Pages (from-to)	68-76
Number of pages	9
Journal	IEEE Transactions on Industry Applications
Volume	40
Issue number	1
DOIs	https://doi.org/10.1109/TIA.2003.821797
State	Published - Jan 2004

Bibliographical note

Funding Information:
Dr. Abido is the recipient of the First Prize Paper Award of the Industrial Automation and Control Committee of the IEEE Industry Applications Society.

Keywords

Digital signal processor (DSP)
Genetic algorithm (GA)
Neuro-fuzzy control
Permanent-magnet synchronous motor
Proportional-integral (PI) controller
Speed control
Vector control

ASJC Scopus subject areas

Control and Systems Engineering
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

UN SDGs

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

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10.1109/TIA.2003.821797

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title = "Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent-Magnet Synchronous Motor Drives",

abstract = "A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an 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 various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition a genetic algorithm is used to optimize the 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 fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN-based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1-hp IPMSM. The effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications in an IPMSM drive.",

keywords = "Digital signal processor (DSP), Genetic algorithm (GA), Neuro-fuzzy control, Permanent-magnet synchronous motor, Proportional-integral (PI) controller, Speed control, Vector control",

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

note = "Funding Information: Dr. Abido is the recipient of the First Prize Paper Award of the Industrial Automation and Control Committee of the IEEE Industry Applications Society.",

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AU - Abido, M. A.

AU - Rahman, M. Azizur

N1 - Funding Information: Dr. Abido is the recipient of the First Prize Paper Award of the Industrial Automation and Control Committee of the IEEE Industry Applications Society.

PY - 2004/1

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N2 - A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an 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 various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition a genetic algorithm is used to optimize the 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 fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN-based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1-hp IPMSM. The effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications in an IPMSM drive.

AB - A hybrid neuro-fuzzy scheme for online tuning of a genetic-based proportional-integral (PI) controller for an 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 various system disturbances. In this work, initially different operating conditions are obtained based on motor dynamics incorporating uncertainties. At each operating condition a genetic algorithm is used to optimize the 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 fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. The proposed FBFN-based PI controller provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed controller is successfully implemented in real time using a digital signal processor board DS 1102 for a laboratory 1-hp IPMSM. The effectiveness of the proposed controller is verified by simulation as well as experimental results at different dynamic operating conditions. The proposed controller is found to be robust for applications in an IPMSM drive.

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KW - Proportional-integral (PI) controller

KW - Speed control

KW - Vector control

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Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent-Magnet Synchronous Motor Drives

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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