Predictive current control of a bearingless induction motor model based on fuzzy dynamic objective function

Abstract

A control strategy for a bearingless induction motor (BL-IM) based on the fuzzy dynamic objective function is proposed in this paper. Firstly, based on the discrete mathematical model of the BL-IM, the stator current and flux linkage are predicted according to the given stator current and flux linkage, the objective function of model predictive current control (MPCC) is designed. Secondly, the fuzzy control algorithm is introduced in the objective function of the MPCC to dynamically assign the weighting factors before the current component on the d- q axis and the influence of the objective function on the performance of the BL-IM is analyzed under different weighting factors. By discretizing the rotational speed deviation Δ ω and rotational speed deviation rate, fuzzy reasoning is performed to obtain the optimal fuzzy dynamic function. Finally, the optimal fuzzy dynamic function is selected as the objective function of the MPCC to perform the simulations and experiments. The results show that the performance of the BL-IM under the MPCC strategy based on the fuzzy dynamic objective function is improved compared with the traditional MPCC and the vector control based on the fuzzy PID, due to its better dynamic and suspension performance. Meanwhile, the stability of rotor current component is enhanced.