Abstract
ABSTRACT Aiming at the rotor mass eccentric vibration caused by mechanical imbalance of a bearingless induction motor (BL-IM), a radial basis function (RBF) neural network strategy is proposed to compensate the rotor displacement. By analysing the principle and influence of the BL-IM’s unbalanced vibration, the detected displacement signal and the set tracking signal are compared to get a difference, then the difference is calculated by the adaptive controller of the RBF neural network to generate a compensation signal to eliminate the vibration component, forcing the rotor to rotate around its geometric centre axis to achieve rotor vibration suppression. The compensation effect of the proposed strategy is verified in MATLAB/Simulink platform and an experimental prototype. The simulation and experimental results show that the rotor displacement peak-to-peak value about 5 µm at 3000 r/min, both simulation and experimental results illustrate that the proposed strategy reduces the peak-to-peak value of the rotor radial vibration displacement and has a good dynamic performance in the wide speed range.
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