Double-Vector Model-Free Predictive Current Control for PMSMs With Influence Rejection of DC Voltage Mismatch

Abstract

High system parameter sensitivity and large current ripple are two key drawbacks that hinder the further development of model predictive control (MPC) for power converters and motor drives. In this article, an improved model-free predictive current control (MFPCC) based on an ultra-local model (ULM) is proposed for permanent magnet synchronous machines (PMSMs). The proposed method is free of dependence on arbitrary motor parameters and influence rejection of dc voltage mismatch since all unknown parameters of the ULM are estimated by current data only. Besides, the optimal voltage vector (VV) problem of MPC is converted to the shortest distance problem by constructing the forced current variation (FCV) domain based on the ULM. On this basis, a double-vector (DV) MFPCC is developed to suppress current ripple, thus reducing torque ripple. On the whole, the proposed method is low-computational and therefore can be deployed in low-cost digital controllers. In the end, a 2-kW experimental setup is built and experiments under different operating conditions are carried out to demonstrate the effectiveness of the proposed method.