MTPA-Based Sequential Model Predictive Control of Induction Motors

Recently, Sequential Model Predictive Control (SMPC) has been proposed in order to eliminate the weighting factors by exploiting the hierarchical structure of cost functions. In conventional SMPC, to meet all load conditions, flux reference is set to the nominal value of the induction motor, leading to suboptimal operation. This paper incorporates Maximum Torque Per Ampere (MTPA) principles with SMPC to generate a proper flux reference. By doing so, the stator current amplitude, and consequently, the losses are reduced in light load conditions. Moreover, the number of voltage vectors (VVs) selected by the first cost function varies between two and three according to the load condition. In order to verify the proposed method, some simulation has been performed, and the obtained results demonstrate the effectiveness of the proposed method in stator current amplitude and loss reduction.