Optimization of a Novel Torque Sharing Function for Improved Switched Reluctance Motor Control Using PSO

Abstract

Switched Reluctance Motors (SRMs) are increasingly adopted in modern electric drive systems due to their mechanical robustness, fault tolerance, and high efficiency. However, their nonlinear magnetic characteristics and discrete torque production often result in pronounced torque ripple, which can limit performance in precision applications. This paper introduces an enhanced optimization strategy for the Torque Sharing Function (TSF) to minimize torque ripple in SRMs. The TSF is essential for regulating instantaneous torque by distributing the reference torque among individual phases according to rotor position, thereby improving torque smoothness and operational stability. The proposed method divides the reference torque into multiple subintervals during phase excitation to control overlap duration and reduce negative torque during commutation. Key control parameters including turn-on, turn-off, and overlap angles are jointly optimized by particle swarm optimization algorithm (PSO) to achieve an effective balance between torque ripple reduction, efficiency, and performance across a wide speed range. Finite element analysis is employed for accurate magnetic characterization, while iterative optimization algorithms ensure consistency, convergence, and robustness. Simulation results validate the proposed framework, showing notable reductions in torque ripple, improved speed regulation, and enhanced dynamic response. The method provides a flexible and scalable control solution applicable to various SRM configurations, promoting advancements in high-performance electric drive systems.