Reduced Kalman filtering for indirect adaptive control of the induction motor

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Abstract
Parameter variations strongly affect the application of indirect field‐oriented control (FOC) of the induction motor. To estimate those parameters which cannot be obtained by means of a direct measure, an augmented state observer can be constructed; in particular, the presence of noise introduced by the inverter and sensors suggests the use of an extended Kalman filter (EKF). In this paper, by analysing the indirect FOC technique via a two‐time‐scale approach, we formally justify its effectiveness and propose a reduced‐order EKF which is used to construct an adaptive version of the FOC method. The practical drawback of reduced‐order EKFs, i.e. the necessity to differentiate measured quantities, is avoided by exploiting some peculiarities of the field‐oriented controller.

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