Microprocessor based intelligent control of induction motors

Abstract

This paper presents a microprocessor-based intelligent controller implemented, in a single processor, for field-oriented induction motor control taking nonlinear parameter variations into account. Previous investigations on this subject have neglected the effect of saturation in the air gap flux and hence the corresponding parameter variation of the induction motor. As the phase angle of rotor magnetizing current (or MMF vector) in a standard induction motor cannot be measured by direct means, an observer is generally needed in the field-oriented control of induction motors. Two types of observers (based on the linear and nonlinear model of the machine) are used in field-oriented induction motor control schemes as can be found in literature. The reduced-order linear model of the observer is easy to implement in real-time, but does not give an accurate estimation of the MMF vector angle, β, since the induction motor operates in the region of saturation. The nonlinear model which incorporates this effect of magnetic saturation of the induction motor cannot be practically implemented by using normal methods as it takes too long a time to estimate the angle β. This paper presents the implementation of a real-time intelligent controller based on artificial neural networks (ANN) which takes into account the effect of saturation and estimates the angle β in a few microseconds which is well within the real-time deadline.