Online Magnetizing Inductance Identification Strategy of Linear Induction Motor Based on Second-Order Sliding-Mode Observer and MRAS

The magnetizing inductance in linear induction motor (LIM) is heavily influenced by the dynamic end effect. Considering that the accuracy of magnetizing inductance usually plays an important role in control system, an online magnetizing inductance estimation strategy for LIM is proposed in this paper. It is based on the back electromotive force (EMF) model reference adaptive system (MRAS) and second-order sliding-mode supertwisting algorithm (STA). Compared to the conventional flux based MRAS identification strategy, there is no pure integration and differential operation in both reference and adaptive models for the proposed method so that integral initial values, DC bias and high-frequency-noise amplification problems can be solved. Stability analysis is given by using the Popov's criterion for hyperstability. Comprehensive simulation and experimental results are given to verify the effectiveness.