Efficient Model Predictive Control for Induction Machine with SOGI-FLL Based Flux Estimator

Predictive torque control (PTC) has a similar design to direct torque control (DTC). Instead of using a look-up switching table and hysteresis comparators, this approach evaluates the torque and stator flux errors as a cost function. It includes the inverter model into the control system and avoids the need for any modulation bloc. Besides, this control technique can handle numerous system constraints in a single control law with weighting variables. However, these coefficients are computed manually. The purpose of this study is to present a simple strategy that makes the tuning of weighting factors unnecessary. This technique uses load angle control to drive the torque. Then, only the flux reference and its predicted quantity will be evaluated by the cost function optimization. Since the control variables have the same type, no weighting factors are required. Moreover, the suggested control method utilizes a second-order generalized integrator (SOGI) as a flux estimator in order to solve the related problems of using a pure integrator or low-pass filters. A simulation with MATLAB/Simulink software is utilized to validate the effectiveness of the proposed strategies.