Implementation of Real-Coded GA-based Fuzzy Controller for Sensorless SR Motor Drive

This paper describes a real-coded GA-based fuzzy controller for controlling speed of the sensorless switched reluctance motor drive. The sensorless operation was modeled using minimal neural network with flux and current as the input and rotor position as output variables. The controller consists of fuzzy controller and real-coded GA. The input, output scaling factor and membership function boundary values of fuzzy controller and turn-off angle of motor are tuned by real-coded GA using the minimum speed error is considered as the fitness function. The proposed control algorithm has been simulated in MATLAB and implemented using TMS320F2812 DSP controller and tested with 1 hp, 8/6 SR motor. To demonstrate the effectiveness, the results of the proposed real-coded GA-based fuzzy controller is compared and analyzed with conventional proportional integral controller and fuzzy controller. Both simulations and experimental results prove that the proposed controller performance is better in terms of steady state response, dynamic response, and robustness than the conventional control methods. The vibration analysis clearly shows that the proposed system will minimize the vibration on motors.