Encoderless FS-PTC for induction motor with extended Kalman filter

This paper proposes an encoderless finite state predictive torque control (FS-PTC). In FS-PTC, stator flux and torque are predicted using a finite number of inverter switching states to select an optimal voltage vector to be applied to the motor by actuating a predefined cost function. Up to now, extended Kalman filter (EKF), a promising state observer for encoderless control system, has not been used with FS-PTC to estimate motor speed and flux, since it needs more calculation time. However, it can be implemented by sacrificing a small amount of torque and flux ripples. Hence, in this paper, EKF is used to estimate rotor speed and flux. Then, the stator flux is estimated from the estimated rotor flux. Measurement noises in the currents are also filtered out through EKF. Simulation results show that the proposed estimator can estimate the speed accurately, at both speed reversal and load change conditions. The sensitivity of the control scheme is also investigated for the deviations of stator and rotor resistances.