Model-Predictive Flux Control of Induction Motor Drives With Switching Instant Optimization

Abstract

Conventional model-predictive torque control (MPTC) requires tedious and time-consuming tuning work for stator flux weighting factor, and presents relatively high torque ripples. To solve these problems, this paper proposes a model-predictive flux control (MPFC) for two-level inverter-fed induction motor (IM) drives. The references of stator flux magnitude and torque in conventional MPTC are converted into an equivalent reference of stator flux vector in the proposed MPFC. As only the tracking error of stator flux vector is required in the cost function, the use of weighting factor is eliminated. The optimal voltage vector is selected based on the principle of stator flux error minimization and its switching instant is optimized rather than being in the beginning of each control period. The proposed MPFC with and without switching instant optimization are both implemented in a 32-bit floating digital signal processor, and they are compared in detail in terms of torque ripple, current harmonics, and average switching frequency. Both digital simulations and experimental tests were carried out on a two-level inverter-fed IM drive, and the obtained results validate the effectiveness of the proposed method.