This paper discusses a model reference adaptive sliding-mode control of the sensorless vector controlled induction motor drive
in a wide speed range. The adaptive speed controller uses on-line trained fuzzy neural network, which enables very fast tracking of the
changing speed reference signal. This adaptive sliding-mode neuro-fuzzy controller (ASNFC) is used as a speed controller in the direct
rotor-field oriented control (DRFOC) of the induction motor (IM) drive structure. Connective weights of the 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 model reference adaptive system (MRAS) - type estimator. Presented simulation results
are verified by experimental tests performed on the laboratory-rig with DSP controller.