Enhanced Sensorless Model Predictive Control of Induction Motor Based on Extended Kalman Filter

This paper presents an enhanced method for a self-sensing (Sensorless) Model Predictive Control (MPC) of Induction Motor (IM) supplied form two-level using state-space model. For the self-sensing process, the controller needs a proper position and speed observer. In this paper, the position and speed of the rotor are estimated based on Extended Kalman Filter (EKF). The proposed EKF is executed to achieve a small error in the estimation parameters of IM in transient and steady-state operation. Model predictive control uses all the mechanical and electrical variables to calculate a cost function for each switching state of the inverter. The voltage that achieves the lowest errors (lowest cost function) is selected to be applied in the next sampling interval. High level of flexibility is obtained using the proposed control technique over a wide speed range with a fast dynamic response. The proposed methodology is validated by simulation results to clarify the ability of the proposed control algorithm in different speed area.     «

This paper presents an enhanced method for a self-sensing (Sensorless) Model Predictive Control (MPC) of Induction Motor (IM) supplied form two-level using state-space model. For the self-sensing process, the controller needs a proper position and speed observer. In this paper, the position and speed of the rotor are estimated based on Extended Kalman Filter (EKF). The proposed EKF is executed to achieve a small error in the estimation parameters of IM in transient and steady-state operation. Mo...     »