Finite-State Model Predictive Current Control for Surface-Mounted Permanent Magnet Synchronous Motors Based on Current Locus

Abstract

Finite-state model predictive control (FS-MPC) is computationally expensive, as it uses all voltage vectors available for prediction and estimation. This paper proposes a novel finite-state model-based predictive current control (MPCC) scheme to overcome the drawbacks of FS-MPC. A reference frame based on the prediction of the current locus when a zero-voltage vector is employed is established to reduce the computational requirements; specifically, only one zero-voltage vector must be predicted compared with the seven required in conventional FS-MPC. The selection of the optimal voltage vector is based on the direction of the current locus in the established reference frame instead of a cost function, which is necessary and time consuming in conventional FS-MPC. Zero-voltage vectors are also selected by contrasting the distance between the reference and predictive currents to reduce the torque ripple of the proposed method. Simulation and experimental results are presented and confirm the efficient performance of the proposed MPCC.