Model predictive control for extended Kalman filter based speed sensorless induction motor drives

Model predictive control (MPC) strategy is a typical optimization predictive control method. MPC can achieve satisfying control performance with lower model parameter dependency, and it has been widely applied in process control systems. To improve the system reliability and reduce the hardware cost, extended Kalman filter (EKF) was introduced to achieve sensorless operation. A novel speed sensorless control system of induction motors which coordinates the MPC current controllers and the EKF state estimator is presented in this paper. The proposed scheme has been verified by simulations, and the results show that excellent speed control performance and state estimation performance over wide speed range and load torque range have been achieved either in dynamics or in steady states.