A Neural Controller for Induction Motors: Fractional-Order Stability Analysis and Online Learning Algorithm

Mohammad Hosein Sabzalian; Khalid A. Alattas; Fayez F.M. El-Sousy; Ardashir Mohammadzadeh; Saleh Mobayen; Mai The Vu; Mauricio Aredes

doi:10.3390/math10061003

A Neural Controller for Induction Motors: Fractional-Order Stability Analysis and Online Learning Algorithm

Mohammad Hosein Sabzalian, Khalid A. Alattas, Fayez F.M. El-Sousy, Ardashir Mohammadzadeh, Saleh Mobayen, Mai The Vu, Mauricio Aredes

Electrical Engineering

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

5 Scopus citations

Abstract

In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics.

Original language	English
Article number	1003
Journal	Mathematics
Volume	10
Issue number	6
DOIs	https://doi.org/10.3390/math10061003
State	Published - 1 Mar 2022

Keywords

Faulty conditions
Fractional calculus
Group method of data-handling neural network
Induction motor
Machine learning
Neural control
Robust control
Stability analysis

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10.3390/math10061003

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title = "A Neural Controller for Induction Motors: Fractional-Order Stability Analysis and Online Learning Algorithm",

abstract = "In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics.",

keywords = "Faulty conditions, Fractional calculus, Group method of data-handling neural network, Induction motor, Machine learning, Neural control, Robust control, Stability analysis",

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T1 - A Neural Controller for Induction Motors

T2 - Fractional-Order Stability Analysis and Online Learning Algorithm

AU - Sabzalian, Mohammad Hosein

AU - Alattas, Khalid A.

AU - El-Sousy, Fayez F.M.

AU - Mohammadzadeh, Ardashir

AU - Mobayen, Saleh

AU - Vu, Mai The

AU - Aredes, Mauricio

PY - 2022/3/1

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N2 - In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics.

AB - In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics.

KW - Faulty conditions

KW - Fractional calculus

KW - Group method of data-handling neural network

KW - Induction motor

KW - Machine learning

KW - Neural control

KW - Robust control

KW - Stability analysis

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A Neural Controller for Induction Motors: Fractional-Order Stability Analysis and Online Learning Algorithm

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Keywords

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