Model Predictive Torque Control for Multilevel Inverter fed Induction Machines Using Sorting Networks

Abstract

Model Predictive Control is a promising technique for electric drive control, as it enables optimization for multiple parameters and offers reliable operation with non-linear systems. For induction machine drives it can be realized using separate cost functions for the torque and the stator flux. Although this eliminates the problem of calculating any weighting factor, the selection of the final voltage vector requires an additional sorting algorithm. By increasing the number of voltage levels or the prediction horizon, the sorting algorithm becomes more and more time-intensive, which can severely impair the performance of the control algorithm. This paper introduces a novel hybrid sorting algorithm consisting of two sorting networks and a merging step. As a case study, the described control method is applied for an induction machine with high rated frequency fed by a three-level inverter, while also discussing the implementation issues. Experimental results verify the operation of the devised sorting algorithm.