Advanced control of five-phase PMSM using combined fractional-order super-twisting algorithm with modified SVM approach

Abstract Multi-phase machine drives are gaining attention in the fields of electrical machines and control due to their benefits. However, the application of these motors is challenging due to the inefficiency of conventional control techniques in multiphase systems. This paper presents an enhanced direct torque control (DTC) method for a five-phase permanent magnet synchronous motor (PMSM). The problems of variable switching frequency and limitations of the proportional integral (PI) controller have been overcome by integrating a fractional-order super-twisting sliding mode algorithm (FOSTSMA) with a modified space vector modulation (MSVM) technique. This MSVM approach determines minimum and maximum voltage values to achieve superior performance. The proposed DTC-MSVM-FOSTSMA strategy significantly outperforms traditional DTC by offering better robustness and an improved dynamic response. Simulation results in MATLAB confirmed the proposed DTC-MSVMFOSTSMA strategy’s efficacy in enhancing motor performance. Compared to conventional DTC and DTC-MSVM-PI methods, this approach yielded substantial improvements: an 83.33% and 78.57% reduction in response time, a 61.84% and 32.55% decrease in torque ripple, and a 58.64% and 34.52% minimization of stator flux ripples, respectively. Additionally, the proposed control exhibits robust performance under parameter variations.

Authors

Institutions

Publication Details

Journal
Journal of Electrical Engineering
Published
2025-12-01
DOI
https://doi.org/10.2478/jee-2025-0057
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advanced control of five-phase PMSM using combined fractional-order super-twisting algorithm with modified SVM approach

Abdelkader Yousfi, Ismail Bouyakoub, Fayçal Mehedi, Zakaria Reguieg
Journal of Electrical Engineering
Sensorless Control of Electric Motors
article

Advanced control of five-phase PMSM using combined fractional-order super-twisting algorithm with modified SVM approach

Abdelkader Yousfi, Ismail Bouyakoub, Fayçal Mehedi, Zakaria Reguieg
article en

Abstract

Abstract Multi-phase machine drives are gaining attention in the fields of electrical machines and control due to their benefits. However, the application of these motors is challenging due to the inefficiency of conventional control techniques in multiphase systems. This paper presents an enhanced direct torque control (DTC) method for a five-phase permanent magnet synchronous motor (PMSM). The problems of variable switching frequency and limitations of the proportional integral (PI) controller have been overcome by integrating a fractional-order super-twisting sliding mode algorithm (FOSTSMA) with a modified space vector modulation (MSVM) technique. This MSVM approach determines minimum and maximum voltage values to achieve superior performance. The proposed DTC-MSVM-FOSTSMA strategy significantly outperforms traditional DTC by offering better robustness and an improved dynamic response. Simulation results in MATLAB confirmed the proposed DTC-MSVMFOSTSMA strategy’s efficacy in enhancing motor performance. Compared to conventional DTC and DTC-MSVM-PI methods, this approach yielded substantial improvements: an 83.33% and 78.57% reduction in response time, a 61.84% and 32.55% decrease in torque ripple, and a 58.64% and 34.52% minimization of stator flux ripples, respectively. Additionally, the proposed control exhibits robust performance under parameter variations.

Journal of Electrical EngineeringVol. 76(6)
Université Djilali Bounaama Khemis Miliana (DZ), Hassiba Benbouali University of Chlef (DZ)
Openalex Percentile: Top 60%
Sensorless Control of Electric Motors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.