Finite Set Model Predictive Control for PWM Rectifiers Based on Data-driven Neural Network Predictor

In this paper, a finite set model predictive control method based on data-driven neural network predictors (DNNPs) is proposed for pulse width modulation (PWM) rectifiers with fully unknown parameters. First, DNNPs are structured based on concurrent learning such that model uncertainties and input gains are identified simultaneously. Secondly, based on the information estimated by the predictors, a finite set model predictive power controller is designed, which is responsible for simplifying the rolling optimization and reducing the computational complexity. Finally, the stability analysis is provided based on input-to-state stability theory, and simulation results are provided to prove the effectiveness of the proposed method.