Novel hybrid recurrent hermite neural network control for switched reluctance motor drive

This paper aims to implement a novel hybrid recurrent Hermite neural network (HRHNN) control for switched reluctance motor (SRM) drive system in order to reduce the torque ripple. 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 novel HRHNN speed control system composed of a combined supervisor control, a recurrent Hermite neural network (RHNN) control and a compensated control is performed to control SRM drive system. The novel HRHNN control system produces smooth torque up to the motor base speed. Finally, the effectiveness of the proposed control schemes is demonstrated by experimental results.