Online Magnetizing Inductance Identification Strategy of Linear Induction Motor Based on Second-Order Sliding-Mode Observer and MRAS
linear induction motor (LIM)， parameter identification， model reference adaptive system (MRAS)， supertwisting algorithm (STA)
Control methods for linear drives
Final Paper
Dinghao Dong / State Key Laboratory of Advanced Electromagnetic Engineering and Technology; Huazhong University of Science and Technology
In this paper, an online magnetizing inductance estimation strategy of linear induction motor (LIM) is proposed, which 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 avoided. The adaptive law is derived by the Popov’s criterion for hyperstability. Preliminary simulation and experimental results are given to test the proposed strategy.
