Speed estimation for an induction motor using the extended Kalman filter

The article describes a discretized extended Kalman filter for speed estimation of a squirrel-cage induction motor. The filter is an observer for linear and non-linear systems and is based on stochastic intromission, in others words, noise. There is a mathematical basis that yields excellent simulation and real-time results. It shows that the filter is an excellent estimator for measurements and non-measurement states because, in addition to speed estimation, we can estimate other variables, like magnetic flux. There are simulation results with direct torque control where the speed accuracy is one of the most important characteristics of filtering. The tune-up is one milestone to obtaining the estimation, and several rules are proposed. Finally, the paper suggests online estimation to ensure that the motor is fully represented.