Hysteresis bound quantization approach in optimal Model Predictive Direct Torque Control of induction machines

Abstract

This paper addresses a novel approach for adapting the concept of Model Predictive Control (MPC) in Direct Torque Control (DTC) of induction motor drives. The proposed algorithm enhances the performance of a two-level inverter DTC controller by keeping the motor's electromagnetic torque and stator flux magnitude within predefined hysteresis bounds while minimizing the switching losses. The MPC controller extracts chains of candidate switching sequences over the prediction horizon. Dynamic programming is implemented to choose the switching sequences that minimize the cost function on power losses. The simulations are performed on accurate models of the motor and controllers and results verify the advantages of the proposed MPDTC method in comparison with classic DTC.