Two efficient Kalman filters for flux and velocity estimation of induction motors

This paper first presents an efficient implementation of the linear Kalman filter used for the rotor flux estimation of an induction motor. This estimator has three parameters which are tuned by an optimization procedure, so as to obtain a good flux estimation in spite of an underestimated rotor resistance. Our approach is then applied to the extended Kalman filter used for the estimation of both the rotor flux and the rotor velocity. Compared with straightforward implementations of these Kalman filters, our algorithms reduce the number of arithmetic operations by a factor 4.7 in the first case and a factor 2.7 in the second case, allowing higher sampling rates and/or cheaper microcontrollers.