Imitation Learning with Stability and Safety Guarantees

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dc.contributor.authorYin, Heen
dc.contributor.authorSeiler, Peteren
dc.contributor.authorJin, Mingen
dc.contributor.authorArcak, Muraten
dc.date.accessioned2022-03-01T18:21:15Zen
dc.date.available2022-03-01T18:21:15Zen
dc.date.issued2022-01-01en
dc.date.updated2022-03-01T18:21:10Zen
dc.description.abstractA method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL). Convex stability and safety conditions are derived for linear time-invariant systems with NN controllers by merging Lyapunov theory with local quadratic constraints to bound the activation functions in the NN. These conditions are incorporated in the IL process, which minimizes the IL loss, and maximizes the volume of the region of attraction associated with the NN controller simultaneously. An alternating direction method of multipliers based algorithm is proposed to solve the IL problem. The method is illustrated on a vehicle lateral control example.en
dc.description.versionSubmitted versionen
dc.format.extentPages 409-414en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/LCSYS.2021.3077861en
dc.identifier.eissn2475-1456en
dc.identifier.issn2475-1456en
dc.identifier.orcidJin, Ming [0000-0001-7909-4545]en
dc.identifier.urihttp://hdl.handle.net/10919/109006en
dc.identifier.volume6en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleImitation Learning with Stability and Safety Guaranteesen
dc.title.serialIEEE Control Systems Lettersen
dc.typeArticleen
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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