Sensorless speed control of switched reluctance motor using brain emotional learning based intelligent controller

In this paper, a brain emotional learning based intelligent controller (BELBIC) is developed to control the switched reluctance motor (SRM) speed. Like other intelligent controllers, BELBIC is model free and is suitable to control nonlinear systems. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed. System responses with BELBIC and FLC are compared. Furthermore, by eliminating the position sensor, a method is introduced to estimate the rotor position. This method is based on Adaptive Neuro Fuzzy Inference System (ANFIS). The estimator inputs are four phase flux linkages. Suggested rotor position estimator is simulated in different conditions. Simulation results confirm the accurate rotor position estimation in different loads and speeds.