Application of neural network in speed control of brushless DC motor using soft switching inverter

Brushless DC motors have a permanent-magnet rotor, and the stator windings are wound such that the back electromotive force (EMF) is trapezoidal. It therefore requires rectangular-shaped stator phase currents to produce constant torque. The Motors possess high torque/weight ratio, operate at very high speed, are very compact and are electronically controlled. The advantage of these motors is the removal of brushes, leading to eliminate many problems associated with brushes. BLDCM drives have been focused on the motor control strategies. Nevertheless, most of these converter topologies employ the hard-switching technique which causes high switching losses and severe electromagnetic interference. Recently, a number of soft-switching techniques, providing zero-voltage switching (ZVS) or zero-current switching (ZCS) condition, have been successfully developed. This paper proposed neural network based speed control of brushless DC motor using soft-switching inverter Hence all switches work in zero voltage switching condition.