Finite Control Set Model-Free Predictive Current Control of PMSM With Two Voltage Vectors Based on Ultralocal Model

Abstract

Model-free predictive current control (MFPCC) that only needs the input and output data of the system can eliminate the influence of the motor parameters mismatch. In the conventional finite control set MFPCC (FCS-MFPCC), the delay and distortion that exist in the current variations update can affect the future current prediction and the current tracking performance. This article studies an improved current variations updating mechanism to improve the update frequency. Based on the ultralocal model, the current variations corresponding to the different voltage vectors can be updated at every control period. Then, the stator currents can be predicted with the accurate current variations, and the optimal voltage vector can be selected by minimizing the cost function without using motor parameters. Besides, a sliding mode observer is designed to observe the ultralocal model's parameters fast. Furthermore, the two voltage vectors and the optimal duration are introduced to improve the current tracking performance. Meanwhile, in order to guarantee the accuracy of the future current prediction, the current compensation strategy is studied to revise the current estimation. Finally, the effectiveness of the proposed FCS-MFPCC strategy is verified through the simulation and experimental results.