Robust Model Predictive Current Control Based on Inductance and Flux Linkage Extraction Algorithm

Abstract

Two-vector model predictive current control (MPCC), which applies two vectors in a control cycle, has better steady state performance than conventional MPCC. However, two-vector MPCC needs to select two optimal vectors, calculate two current slopes and two vector working times, which have to use the inductance and the flux linkage parameter in the permanent magnet synchronous motor (PMSM) drives. Therefore, the control performance of two-vector MPCC is more influenced by the model parameter accuracy. Aiming at reducing parameter sensitivity of the two-vector MPCC method, a robust two-vector MPCC method is proposed, which can obtain accurate inductance and flux linkage information in real time based on the presented inductance extraction algorithm and the flux linkage extraction algorithm. Moreover, the proportional-integral regulator parameters of the extraction algorithms are theoretically deduced. The experiment results of the proposed method indicate that the satisfactory control performance can be achieved under the condition of the parameter mismatches.