Visible to the public Black-Box Modelling of a DC-DC Buck Converter Based on a Recurrent Neural Network

TitleBlack-Box Modelling of a DC-DC Buck Converter Based on a Recurrent Neural Network
Publication TypeConference Paper
Year of Publication2020
AuthorsRojas-Dueñas, G., Riba, J., Kahalerras, K., Moreno-Eguilaz, M., Kadechkar, A., Gomez-Pau, A.
Conference Name2020 IEEE International Conference on Industrial Technology (ICIT)
Date Publishedfeb
KeywordsArtificial neural networks, autoregressive processes, black box encryption, black-box approach, black-box model, black-box modelling, black-box models, composability, DC-DC buck converter, DC-DC power converter, DC-DC power convertors, Metrics, multilayer perceptrons, Neural Network, nonsynchronous buck converter model, power converter, power engineering computing, prediction, pubcrawl, radial basis function networks, recurrent neural nets, recurrent neural network, recurrent nonlinear autoregressive exogenous neural network, Resiliency, static behavior, switching convertors, System Identification, Training
AbstractArtificial neural networks allow the identification of black-box models. This paper proposes a method aimed at replicating the static and dynamic behavior of a DC-DC power converter based on a recurrent nonlinear autoregressive exogenous neural network. The method proposed in this work applies an algorithm that trains a neural network based on the inputs and outputs (currents and voltages) of a Buck converter. The approach is validated by means of simulated data of a realistic nonsynchronous Buck converter model programmed in Simulink and by means of experimental results. The predictions made by the neural network are compared to the actual outputs of the system, to determine the accuracy of the method, thus validating the proposed approach. Both simulation and experimental results show the feasibility and accuracy of the proposed black-box approach.
Citation Keyrojas-duenas_black-box_2020