Trajectory extension methods for model predictive direct torque control

Abstract

This paper focuses on model predictive direct torque control (MPDTC), which is a recent control scheme for three-phase ac electric drives combining the notions of model predictive control (MPC) and direct torque control (DTC). Using a dynamic model of the drive, MPDTC predicts several future switch transitions, extends the outputs and chooses the inverter switch positions that minimize the switching frequency or the switching losses. The performance of MPDTC depends on the accuracy of the predictions. However, MPTDC schemes with very accurate predictions are computationally demanding necessitating very fast controller hardware. New methods for extending the output trajectories are proposed that yield fast yet accurate predictions giving rise to a computationally efficient MPDTC scheme. The advantages of the proposed methods are shown in terms of the associated computational complexity and the accuracy of the predictions.