| In this thesis, a novel neuro-fuzzy controller (NFC) has been developed for speed control of IM. For the complete drive, the indirect field orientation control is utilized in order to decouple the torque and flux controls. Thus, the induction motor can be controlled like a do motor and hence the high performance can be achieved without lacking the advantage of ac over do motors. The proposed neuro-fuzzy controller incorporates Sugeno model based fuzzy logic laws with a five-layer artificial neural network (ANN) scheme. The controller is designed for low computational burden, which will be suitable for real-time implementation. Furthermore, for the proposed NFC an improved self-tuning method is developed based on the IM theory and its high performance requirements. The main task of the tuning method is to adjust the parameters of the fuzzy logic controller (FLC) in order to minimize the square of the error between actual and reference output. In this thesis, a model reference adaptive flux (MRAF) observer is also developed to estimate the d-axis rotor flux linkage in both constant flux and flux weakening regions based on motor voltage, current and reference trajectories for flux linkage. (Abstract shortened by UMI.). |
