ML for Loop Gain Identification of DC/DC Converters

Control loop identification is necessary for evaluating the stability of switched power supplies and is therefore an important step during design and verification. Analytical models of power supplies often yield inaccurate predictions of the loop gain; therefore, power engineers traditionally must conduct slow, invasive loop gain measurements on physical hardware. This thesis presents an alternate approach to loop gain identification in which a machine learning model infers the frequency-domain loop response from the quick and convenient time-domain measurement of a load step transient. We show that we can train a neural network to accurately infer the loop gain of a current-mode buck converter over a generalized set of configurations and illustrate the disruptive potential of such a model with example applications such as live Bode plot monitoring and automatic loop compensation.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Chu, Cecelia

Advisors dc:contributor.advisor

• Perreault, David J.
• Lu, Wenjie

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/143266

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/143266