Speed and rotor flux estimation of induction motors via on-line adjusted extended Kalman filter

This paper deals with the estimation of speed and rotor flux of induction motors via Extended Kalman Filter (EKF) with on-fine adjusting of the system noise covariance matrix. The predictor of EKF consists of a discrete time model obtained by means of a second order discretization of the original nonlinear model of the induction motor. In order to obtain accurate estimation of the above mentioned variables, the load torque is included in the state variables and then estimated. Three different system noise models are also illustrated and compared each other by simulations carried out in Matiab/Simulink environment For one of these models, EKF is adjusted on-line by means of an additional PID-type control loop driven by the stator current error which gives updates of the system noise covariance matrix.