A Novel Finite-Control-Set Model Predictive Current Control for Five-Phase PM Motor With Continued Modulation

Abstract

The conventional five-phase finite-control-set model predictive control (FCS-MPC) suffers from heavy computational burden. Meanwhile, the control set with finite vectors inevitably leads to deteriorated operation performance. This article proposes a novel FCS model predictive current control (MPCC) with continued modulation. A cascaded optimization procedure is proposed, which includes main virtual voltage vector (VV) selection, determination of optimal combination of adjacent two VVs, and amplitude optimization. In this cascaded optimization procedure, the phase angle and the amplitude of the synthesized vector can be obtained in different steps. Also, there are two duty cycles calculated in different steps independently, which avoid overflow. By using this cascaded procedure, the continued modulation can be realized without using a modulator. Meanwhile, a vector selection method is introduced to reduce computational burden. This vector selection method avoids evaluation of all the candidate vectors without any negative effects. Moreover, the merits of conventional FCS-MPCC can be preserved. The experimental results verify the effectiveness and superiority of the proposed FCS-MPCC method.