A Novel Deep Learning Controller for DC-DC Buck-Boost Converters in Wireless Power Transfer Feeding CPLs

Wireless power transfer (WPT), in a general scene, is the transmission of electrical energy for various technologies by means of magnetic couple resonance. Its potential applications change from low power electric equipment (like smartphones) to high-power devices over short distances of centimeters to tens of centimeters. In this letter, a pulsewidth-modulation-based adaptive data-driven strategy is newly developed for the output voltage regulation of the series-series (SS) compensated WPT. In an attempt to achieve a fast and adaptive voltage regulation, a deep deterministic policy gradient (DDPG) based on an ultralocal model (ULM) control scheme is applied to the dc-dc buck-boost converter feeding constant power loads installed in the receiver side of the WPT. In the suggested scheme, an intelligent feedback controller based on the ULM is used to stabilize the voltage response of the buck-boost converter while the unknown dynamics of the converter are estimated by a sliding mode observer (SMO). An auxiliary deep DDPG algorithm with Actor-Critic architecture is implemented to employ its adaptive capability to eliminate the SMO estimation error and improve the voltage regulation of the WPT plant. Some experimental outcomes on a laboratory testbed of the WPT plant are presented to assess the merits and real-time implementation of the adaptive data-driven scheme from a systematic perspective.

Automated summary

This study introduces a novel pulsewidth-modulation-based adaptive data-driven strategy for voltage regulation in wireless power transfer systems, using deep learning techniques. Experimental outcomes demonstrate the effectiveness of the proposed scheme in improving voltage regulation of the system.