Adaptive sliding-mode neuro-fuzzy control of the sensorless induction motor drive with MRASCCestimator

In the paper a model reference adaptive sliding-mode control using on-line trained fuzzy neural network is applied to the sensorless induction motor drive system with MRAS type speed estimator. In this control structure adaptive sliding-mode neuro-fuzzy controller (ASNFC) is used as a speed controller, in the direct field oriented control structure. Connective weights of this controller are trained on-line according to the error between the actual speed of the drive and the reference model output signal. The rotor flux and speed of the vector controlled induction motor are estimated using the novel MRAS^CCestimator. Simulation results are verified in experimental tests.