A particle swarm optimization based maximum torque per ampere control for a Switched Reluctance Motor

The Switched Reluctance Machine (SRM) is known for being one of the oldest electric machine designs. Unfortunately, it is usually assumed that this implies that the machine is outdated. However with the advent of microprocessors, the SRM has become a suitable option for a number of applications because the shortcomings of the machine can be mitigated with control. Compared to other machines, the SRM is more rugged, has a simpler structure, and is less expensive to manufacture. The machine has two control regions: when the speed of the machine is beneath a value called the base speed and when the speed is above the base speed. The base speed is the speed at which the back electromotive force (EMF) of the motor becomes substantial when compared to the source voltage. In both regions, the turn-on and turn-off angles of the machine can be used to control the machine. This thesis proposes a method of generating optimal turn-on and turn-off angles.;The method presented in this thesis is concerned with finding the turn-on and turn-off angles needed to generate maximum torque per ampere (MTA). The strategy applies a particle swarm optimization (PSO) technique that searches for the angles that maximize the inductance of the SRM in order to achieve MTA. The inductance function was obtained via Finite Element Analysis (FEA) and experimentally. The method was applied to a 4-phase 8/6 SRM. The proposed strategy was found to be effective at both low speeds (beneath the base speed) and high speeds (above the base speed), but MTA could only be asserted for low speeds.