Electronic circuit comprising an artificial neural network and method therefor

A hardware-based neural network optimizes switching converter control by determining duty cycles independently of previous cycles, addressing bandwidth limitations and component size issues, achieving efficient and stable operation.

WO2026131789A1PCT designated stage Publication Date: 2026-06-25TECHNISCHE UNIVERSITAT DRESDEN
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
TECHNISCHE UNIVERSITAT DRESDEN
Filing Date
2025-12-16
Publication Date
2026-06-25

AI Technical Summary

Technical Problem

Conventional switching converters have limited control bandwidth, leading to higher overshoot or the need for large, expensive capacitors, and are unstable at higher control bandwidths.

Method used

Implementing a hardware-based, trained artificial neural network to determine the duty cycle for switching converters, using a simulation model that replicates the external circuit's behavior to optimize control without linearized models.

Benefits of technology

Achieves a control bandwidth of 100% of the switching frequency, reducing component size and cost, eliminating electrolytic capacitors, and extending service life while maintaining efficiency and stability.

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Abstract

According to various aspects, an electronic circuit (100) is provided, wherein the electronic circuit (100) comprises: an input interface (104) and an output interface (106) for coupling to an external circuit (160); and a trained artificial neural network (102) designed to: receive one or more input signals (110, 112) which represent one or more operating parameters (120) of the external circuit (160) at a predefined point in time within a first cycle of a duty cycle signal; determine a duty cycle (122) to be used in a second cycle of the duty cycle signal after the first cycle; and provide an output signal (114, 116) to the external circuit (160) which represents the determined duty cycle.
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