Torque-ripple reduction in switched reluctance motor drive using SHRFNN control

The purpose of this paper is to investigate and implement a novel approach to learning control for torque-ripple reduction of switched reluctance machines (SRM) using a supervisor hybrid recurrent fuzzy neural network (SHRFNN) control. First, the dynamic models of a SRM drive system are builted though SRM experimental tests and parameters measurements. Then, in order to reduce torque ripple and control robustness, a SHRFNN speed control system that combined supervisor control, RFNN and compensated control is developed to control SRM drive system. The SHRFNN control system produces smooth torque up to the motor base speed. The torque is generated over the maximum positive torque-producing region of a phase. Finally, the effectiveness of the proposed control schemes is demonstrated by experimental results.