LAPSE:2023.23920
Published Article
LAPSE:2023.23920
Continuous Control Set Model Predictive Control of a Switch Reluctance Drive Using Lookup Tables
Alecksey Anuchin, Galina L. Demidova, Chen Hao, Alexandr Zharkov, Andrei Bogdanov, Václav Šmídl
March 27, 2023
A problem of the switched reluctance drive is its natural torque pulsations, which are partially solved with finite control set model predictive control strategies. However, the continuous control set model predictive control, required for precise torque stabilization and predictable power converter behavior, needs sufficient computation resources, thus limiting its practical implementation. The proposed model predictive control strategy utilizes offline processing of the magnetization surface of the switched reluctance motor. This helps to obtain precalculated current references for each torque command and rotor angular position in the offline mode. In online mode, the model predictive control strategy implements the current commands using the magnetization surface for fast evaluation of the required voltage command for the power converter. The proposed strategy needs only two lookup table operations requiring very small computation time, making instant execution of the whole control system possible and thereby minimizing the control delay. The proposed solution was examined using a simulation model, which showed precise and rapid torque stabilization below rated speed.
Keywords
continuous control set, electrical drive, magnetization surface, Model Predictive Control, pulse-width modulation, switched reluctance motor drive
Suggested Citation
Anuchin A, Demidova GL, Hao C, Zharkov A, Bogdanov A, Šmídl V. Continuous Control Set Model Predictive Control of a Switch Reluctance Drive Using Lookup Tables. (2023). LAPSE:2023.23920
Author Affiliations
Anuchin A: Department of Electric Drives, Moscow Power Engineering Institute, 111250 Moscow, Russia; Faculty of Control Systems and Robotics, ITMO University, 197101 Saint Petersburg, Russia
Demidova GL: Faculty of Control Systems and Robotics, ITMO University, 197101 Saint Petersburg, Russia
Hao C: School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China
Zharkov A: Department of Electric Drives, Moscow Power Engineering Institute, 111250 Moscow, Russia
Bogdanov A: Faculty of Control Systems and Robotics, ITMO University, 197101 Saint Petersburg, Russia
Šmídl V: Department of Adaptive Systems, Institute of Information Theory and Automation, CZ-182 00 Prague, Czech Republic
Journal Name
Energies
Volume
13
Issue
13
Article Number
E3317
Year
2020
Publication Date
2020-06-29
Published Version
ISSN
1996-1073
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PII: en13133317, Publication Type: Review
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LAPSE:2023.23920
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doi:10.3390/en13133317
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