Application of a neural predictive controller in boost converter input current control

The application of a nonlinear predictive controller in the cascade control structure of a boost converter is investigated. The neuro-predictive controller is realized as a nonlinear optimizer, using the Levenberg-Marquardt unconstrained optimization procedure. For the prediction of future process responses, two MLP neural networks are used. One network is used for modeling the converter dynamics and the other for online estimation of the converter's input voltage. This structure ensures good system performance in all operating regions and inherent compensation of ripples in the converter's input current caused by variations of the input voltage. The advantages of the proposed control structure are demonstrated through experimental comparison with a linear GPC with manually adjusted feedforward compensator.