Machine Learning Based Adaptive Prediction Horizon in Finite Control Set Model Predictive Control

In this paper, an adaptive prediction horizon approach based on machine learning is presented for the finite control set model predictive control (FCS-MPC) of power converters. Usually, in FCS-MPC, the prediction horizon is kept constant. A large prediction horizon improves performance, however, it significantly increases the computational cost. The prediction horizon is typically chosen to be just large enough to give the required performance. We present a novel technique, where the prediction horizon adapts to the states of the converter. We define a cyber-physical objective function that penalizes both the error in converter performance and computational complexity. We perform several offline simulations to find the optimal prediction horizon based on the instantaneous state of the converter, based on a cyber-physical objective function. An artificial neural network is trained to calculate the optimal prediction horizon in run time. The proposed scheme allows a varying prediction horizon that reduces the overall computational complexity, while guaranteeing the required physical performance. The simulations and experimental results of the proposed technique justify the usefulness of our approach.