{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/143266"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/143266","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"ML for Loop Gain Identification of DC/DC Converters","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Chu, Cecelia"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Perreault, David J.","Lu, Wenjie"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-02","date_published":"2022-02","updated_at":"2026-07-22T22:22:10Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/143266","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Perreault, David J.","Lu, Wenjie"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Chu, Cecelia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-06-15T13:08:24Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-06-15T13:08:24Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-02"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/143266"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["ML for Loop Gain Identification of DC/DC Converters"]}]}],"canonical_facts":{"dc:contributor.advisor":["Perreault, David J.","Lu, Wenjie"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Chu, Cecelia"],"dc:date.accessioned":["2022-06-15T13:08:24Z"],"dc:date.available":["2022-06-15T13:08:24Z"],"dc:date.issued":["2022-02"],"dc:description.abstract":["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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/143266"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["ML for Loop Gain Identification of DC/DC Converters"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:22:10Z"}