Experimental Assessment of a Tiny AI-Empowered Output Filter Parameter Extraction Framework for Digital Power

Abstract

An experimental assessment of a ''plug-and-play'' tiny artificial-intelligence-empowered output filter parameter extraction framework for digital power is presented. The framework can be incorporated into an existing digital controller to perform online parameter extraction without adding extra sensors or modifying the power conversion stage. The idea is based on firstly transferring the control from the default control law to the framework, which consists of a predefined control law to perform output regulation for a few switching cycles, then introducing a small-signal perturbation into the control signal, and finally utilizing a long short-term memory (LSTM) network to recognize the dynamic response of the control signal to perform either regression or classification of filter parameters. The LSTM network is trained with a reconfigurable output filter. The proposed framework has been successfully evaluated on a 240W, 100V/48V buck DC/DC converter prototype. The framework's performance is studied by extracting the parameters of a second-order output filter and a fourth-order output filter. For the second-order output filter, the root-mean-square errors (RMSEs) in performing the filter inductor and capacitor regressions are 2.35% and 2.25%, respectively, and the F1-scores in classifying the inductance and capacitance are 0.805 and 0.815, respectively. The framework occupies 0.93% of the memory space of the controller with 512kB flash memories. The extraction time is 17.3ms. For the fourth-order filter, the maximum RMSEs in performing the regression of the filter inductors and capacitors are 2.04% and 6.49%, respectively. The framework occupies 3.62% of the memory space and the extraction time is 328ms.