Neural Network Based Sensorless Position Estimation of Switched Reluctance Motor

The phase excitation pulse of the Switched Reluctance Motor (SRM) must be synchronized with the angular rotor position to ensure the continuous torque and rotation of the rotor and also to obtain the optimum performance of the SRM drive. In this paper Artificial Neural Network (ANN) based sensorless rotor position estimation technique is presented to fulfill the requirement of the position feedback for the SRM. MATLAB simulink environment is used to design a neural network and to simulate the proposed sensorless method. Flux linkage-phase current-rotor position data set is used for training and testing of the ANN. In this paper comparison of algorithm is presented.