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 and Mauricio Aredes
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Mohammad Hosein Sabzalian: Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Khalid A. Alattas: Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia
Fayez F. M. El-Sousy: Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 16273, Saudi Arabia
Ardashir Mohammadzadeh: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Saleh Mobayen: Future Technology Research Center, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Mai The Vu: School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
Mauricio Aredes: Laboratory of Power Electronics and Medium Voltage Applications (LEMT), The Alberto Luiz Coimbra Institute for Graduate Studies and Research in Engineering (COPPE), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941594, Brazil

Mathematics, 2022, vol. 10, issue 6, 1-17

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: neural control; group method of data-handling neural network; robust control; stability analysis; induction motor; faulty conditions; fractional calculus; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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