Finite Control Set Model-Free Predictive Current Control Based on Ultralocal Model and RLS Algorithm for PMSM

In the conventional finite control set model-based predictive current control, the dependence on PMSM parameters will seriously worsen the performance of this algorithm. To overcome this issue, this paper proposes a novel finite control set model-free predictive current control (FCS-MFPCC), with ultralocal model replacing PMSM model, which only considers the mapping relationship between the system input and output. Then, two recursive least squares (RLS) algorithms with different update conditions are applied to estimate the parameters in the ultralocal model. Thus, future current of PMSM can be predicted by using ultralocal model. At the same time, in order to balance the contradiction between the stability and rapidity of the RLS algorithm, the extended state observer (ESO) is used to eliminate the error of parameters estimation. The application of ESO also improves the convergence rate and robustness of the proposed algorithm. Experiment results verify the effectiveness of the proposed algorithm.