Sector-Optimized Model Predictive Torque Control of Five-Phase SRM with Torque Sharing Function

Ke Li

doi:10.1007/s42835-026-02593-3

Sector-Optimized Model Predictive Torque Control of Five-Phase SRM with Torque Sharing Function

2026-02-01

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DOI：10.1007/s42835-026-02593-3

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This paper proposes a Model Predictive Torque Control (MPTC) strategy for a five-phase Switched Reluctance Motor (SRM) that combines sector optimization with an enhanced Torque Sharing Function (TSF). This strategy aims to enhance system efficiency while mitigating torque ripple. First, an LUT-based torque prediction scheme is formulated. Subsequently, sector optimization is applied to partition an electrical cycle into 15 sectors, which effectively reduces the number of candidate voltage vectors (CVVs) and improves system efficiency. Finally, considering the characteristics of three-phase and two-phase alternating conduction in five-phase SRM, an improved TSF is designed to enhance torque-tracking performance and suppress torque ripple. Experimental validation on a 10/8 five-phase SRM prototype demonstrates that the proposed strategy outperforms conventional control methods in terms of torque ripple, efficiency, and dynamic response. This strategy provides innovative insights into the control of five-phase SRM.

Keywords:

Model predictive torque control

Five-phase SRM

Sector optimization

Torque Sharing Function

Torque ripple

Sector-Optimized Model Predictive Torque Control of Five-Phase SRM with Torque Sharing Function

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