Research on Sliding Mode Variable Structure Model Reference Adaptive System Speed Identification of Bearingless Induction Motor

Abstract

To improve the speed observation accuracy of the bearingless induction motor (BL-IM) and achieve its high-performance speed sensorless control, an improved sliding mode variable structure model reference adaptive system speed identification method based on sigmoid function (sigmoid-VS-MRAS) is proposed. Firstly, to overcome the problem of initial values and cumulative errors in the pure integration link of the reference flux-linkage voltage model, the rotor flux-linkage reference voltage model has been improved by using an equivalent integrator instead of the pure integration link. Then, in order to improve the rapidity and robustness of speed identification, the sliding mode variable structure adaptive law is adopted instead of the PI adaptive law. In addition, in order to optimize the sliding mode variable structure adaptive law and overcome the sliding mode chattering problem, a sigmoid function with smooth continuity characteristics is used instead of the sign function. Finally, on the basis of the inverse system decoupling control of a BL-IM, simulation experiments were conducted to verify the sigmoid-VS-MRAS speed identification method. The research results indicate that when the proposed speed identification method is adopted, not only higher identification accuracy and rapidity can be achieved than traditional PI-MRAS methods, but it can also eliminate the problem of high-frequency vibration (with an amplitude of about 3.0 r/min) when using the sign-VS-MRAS method; meanwhile, the steady-state tracking speed with zero deviation can still be maintained after loading.

Keywords

• bearingless induction motor
• sliding mode variable structure adaptive law
• model reference adaptation system
• inverse system decoupling
• speed identification