A new TLS based MRAS speed estimation with adaptive integration for high performance induction machine drives

This paper presents a new MRAS speed observer for high performance FOC induction motor drives which employs the flux error for estimating the rotor speed, but overcomes the pure integration problems by using a novel adaptive integration method based on neural adaptive filtering. A linear neuron (the ADALINE) is employed for the estimation of both the rotor speed and the rotor flux-linkage using a recursive TLS (Total Least Squares) algorithm (the TLS EXIN neuron) for on-line training. This neural model is also used as a predictor with no feedback loops between the output of the neural network and its input. The proposed scheme has been implemented in a test setup and compared with an MRAS OLS (Ordinary Least Squares) speed estimation with low-pass filter integration and with the well-known Schauder's scheme. The experimental results show the in the high and medium speed ranges with and without load, the three algorithms give practically the same results, while in low speed ranges (i.e. below 10 rad/s) the TLS based algorithm outperforms the other two algorithms. Experiments have also been made to test the robustness of the algorithm to load perturbations and to test its performance at zero speed operation.