Abstract
Performance analysis of the sensorless adaptive sliding-mode neuro-fuzzy control of the induction motor drive with MRAS-type speed estimator 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Bulletin of the Polish Academy of Sciences: Technical Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
