Sensorless nonlinear control of induction motors using Unscented Kalman Filtering

Sensorless control for induction motors using Unscented Kalman Filtering is studied. The complete 6-th order dynamic model of the induction motor is analyzed and a nonlinear controller based on differential flatness theory is developed. The Unscented Kalman Filter is proposed to estimate the state vector of the nonlinear electric motor using a limited number of sensors, such as the ones measuring stator currents. Next, control of the induction motor is implemented through feedback of the estimated state vector. The efficiency of the Unscented Kalman Filter-based control scheme, is tested through simulation experiments.