Experimental performance of a model reference adaptive flux observer based NFC for IM drive

This paper presents a model reference adaptive flux (MRAF) observer based neuro-fuzzy controller (NFC) for an induction motor (IM) drive. An improved observer model is developed based on a reference flux model and a closed-loop Gopinath model flux observer which combines current and voltage model flux observers. The d-axis reference flux linkage of the indirect field oriented control is provided by flux weakening method. Furthermore, a proportional-integral (PI) based flux controller is used to provide the compensation for the reference flux model by comparing the flux reference and the observed flux from Gopinath model flux observer. An improved self-tuned NFC is utilized as a speed controller for IM drive. The proposed NFC incorporates fuzzy logic laws with a five-layer artificial neural network (ANN) scheme. In the proposed NFC, parameters of the 4^th layer are tuning online for the purpose of minimizing the square of the error. Furthermore, the design of normalized inputs makes the proposed NFC suitable for variant size of IM with a little adjusting. A complete simulation model for indirect field oriented control of IM incorporating the proposed MRAF observer based NFC is developed in Matlab/Simulink. The performances of the proposed IM drive is investigated extensively at different dynamic operating conditions such as step change in load, step change in change in speed, parameter variations, etc. The proposed IM drive is also implemented in real-time using DSP board DS1104 for a laboratory 1 HP IM. The performance of the proposed MRAF observer based NFC controller is found robust and potential candidate for high performance industrial drive applications.