A speed estimation method for induction motors based on Strong Tracking Extended Kalman Filter

Abstract

A speed estimation method for induction motors based on Strong Tracking Extended Kalman Filter (STEKF) is proposed in this paper, implementing optimization of speed sensorless vector control. With this method, the fading factor is introduced into the covariance matrix of the predicted state, which forces the residual sequences orthogonal to each other and tunes the gain matrix online. The estimation error is adjusted adaptively, and the mutational state is tracked fast. The proposed method shows more robust against the model uncertainties or the time-varying parameter systems, and it has better tracking ability to the mutations and the slow changes. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.