Black-Box Large-Signal Average Modeling of DC-DC Converters Using NARX-ANNs

Abstract

This paper investigates the use of non-linear autoregressive exogenous (NARX) artificial neural networks (ANNs) to achieve black-box average dynamic models of dc-dc converters capable of capturing the main converter non-linearities. Non-linearities may include, for example, dynamic behavior variations due to changes of operating point or operating mode (e.g., discontinuous conduction mode, continuous conduction mode). This paper presents design guidelines for determining the NARX-ANN architecture and the dataset to be used in the training process. Dataset definition includes the choice of the perturbations for stimulating the aimed system behaviors and optimizations for dataset size reduction. The proposed approach is first derived for a dc-dc boost converter. To verify the generality of the proposed method, the same methodology is also applied to a Ćuk converter. In both cases, the proposed NARX-ANN modeling provided accurate results, with only limited deviations observed in the time-domain responses to step variations of duty-cycle and output current. The proposed model provided accurate small-signal behavior under different operating conditions. The validity of the approach is evaluated experimentally by considering a boost converter prototype.

Keywords

• Non-linear autoregressive exogenous (NARX)
• artificial neural network (ANN)
• blackbox average model of power converters
• discontinuous conduction mode (DCM)
• continuous conduction mode (CCM)