Speed control and torque ripple minimization in switch reluctance motors using context based adaptive neuro-fuzzy controller

Switched reluctance (SR) drive technology is seriously challenging existing technologies, because of its technical and economic advantages. If some remaining problems like excessive torque ripple are resolved through intelligent control, it would be an excellent replacement for existing systems, and would probably grab a significant market share. This paper addresses the problems of speed control and torque ripple minimization in switch reluctance motors (SRM), and proposes an adaptive context based neuro-fuzzy controller. A model of SRM is developed and an adaptive control algorithm is described, enabling speed tracking while minimizing the torque ripple. Our results show superior control characteristics including very fast responses, simple implementation and robustness. Our proposed method enables the designer to shape the response in accordance with multiple objectives.