Novel Performance Driven Control of Switched Reluctance Motors Using an Adaptive Hybrid Intelligent Direct Torque Control Minimum Sector Selection Scheme

Switched Reluctance Motors (SRMs) offer robust and cost-effective solutions for various applications due to their simple structure, high efficiency, and fault tolerance. Effective torque and flux control in SRMs provides benefits such as high torque density, wide speed range operation, and improved efficiency. However, challenges remain in mitigating torque ripple, achieving precise flux regulation, and handling non-linear magnetic characteristics. Direct torque control (DTC) approach SRM for simple control implementation and improved steady-state torque performance compared to conventional control schemes. DTC offers high torque and flux control accuracy, robustness against parameter variations and disturbances, and provides a low dynamic response. However, DTC techniques can result in high torque ripple due to using two hysteresis torque and flux bands within the limited loop, improper selection of sectors, and voltage vector (VV) switching. To address this issue, this paper proposes a DTC approach based on a sixteen VV selection, 8 sectors using an adaptive neuro-fuzzy inference system (ANFIS) torque band. Additionally, two switching instant regulators are introduced to further enhance performance. These regulators not only keep the torque within the hysteresis bounds but also minimize torque ripple without increasing the average switching frequency. The proposed solutions are compared to conventional DTC schemes, and experimental evaluations demonstrate significant reductions in torque ripple, flux ripple, and current distortion.