Model-Predictive Control of Induction Motor Drives: Torque Control Versus Flux Control

Abstract

Recently, model-predictive torque control (MPTC) has been introduced as a powerful control method for induction motor drives. However, the weighting factor for stator flux must be tuned carefully to obtain satisfactory performance at different operation points. Unfortunately, so far the tuning of weighting factor in MPTC is mostly based on an empirical procedure. This paper solves this problem by proposing a model-predictive flux control (MPFC), which uses the stator flux vector as the control variable and eliminates the complicated prediction of stator current at $k+2$ instant. As a result, the weighting factor in conventional MPTC is eliminated, and the control complexity is significantly reduced. Both MPTC and MPFC are tested and compared in detail, including steady-state performance, dynamic response, and low-speed operation. The simulation and experimental results prove that the performance of conventional MPTC is dependent on the weighting factor, and the improper weighting factor would lead to significant performance deterioration. On the contrary, the proposed MPFC achieves similar or even better overall performance over a wide speed range with very low tuning work. Hence, it is concluded that the proposed MPFC is more practical than conventional MPTC.