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Experimental Evaluation of an AI Model Trained with Switching Pattern Based on Long-Horizon Model Predictive Control

During the past decade, model predictive control (MPC) for power electronics has been the subject of considerable attention in the power electronics and motor drive community. MPC for power electronics have been studied to improve the performance of its switching frequency of direct torque control (DTC). There is a disadvantage when using original MPC in that the method requires to solve the optimization problem by manipulating voltage vectors and their duration time. Computational issues become especially important for long prediction horizons since the number of possible switching sequences grows exponentially as the prediction length increases. As a result, the prediction horizon is usually set to one when reference tracking of the stator current or the motor torque is being considered. Recently, there is growing interest in the power electronics community with respect to the use of an artificial intelligence (AI) model for optimal and predictive controllers. This paper proposes a speed control system for an induction motor (IM) involving the deployment of an AI model trained with switching patterns based on long-horizon MPC. The proposed system, which involves the use of an AI model, makes possible the calculation of four switching states and three duration times during every sampling time. Additionally, the duration time is calculated by means of high resolution. Finally, the control performance of the proposed system is evaluated the simulation and experiment results.