A Model-Free Deadbeat Predictive Current Control for PMSM Based on Adaptive Current Variation Components Considering Sampling Noise

The performance of the conventional deadbeat predictive current control (DPCC) method heavily depends on the accuracy of the motor model. To overcome this problem, a model-free deadbeat predictive current control (MFDPCC) technology based on measuring current variations with reliable prediction accuracy is proposed. The proposed method takes the natural response component of current variation as an adaptive variable, and the measured current variation as a constraint term for each component, thereby achieving the goal of separating the current variations components. Furthermore, the forced response component of current variation is utilized to calculate the unit voltage current variation and predict the reference voltage vector. To ensure prediction accuracy, the integral effect of current prediction error is designed as the adaptive rate of the natural response component, and the average filtering principle is used to suppress the impact of current sampling noise on performance. Compared to conventional DPCC and MFDPCC based on ultralocal models, the proposed strategy only involves sampling information without any observer or motor parameters, thus completely eliminating the dependence on the motor model. The experiments have shown that the steady-state performance of the proposed strategy is superior to conventional DPCC and MFDPCC based on ultralocal models.