Optimal Energy Shaping via Neural Approximators

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dc.contributor.authorMassaroli, Stefanoko
dc.contributor.authorPoli, Michaelko
dc.contributor.authorCalifano, Federicoko
dc.contributor.authorPark, Jinkyooko
dc.contributor.authorYamashita, Atsushiko
dc.contributor.authorAsama, Hajimeko
dc.date.accessioned2022-12-23T02:00:36Z-
dc.date.available2022-12-23T02:00:36Z-
dc.date.created2022-12-23-
dc.date.created2022-12-23-
dc.date.created2022-12-23-
dc.date.issued2022-
dc.identifier.citationSIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS, v.21, no.3, pp.2126 - 2147-
dc.identifier.issn1536-0040-
dc.identifier.urihttp://hdl.handle.net/10203/303609-
dc.description.abstractWe introduce optimal energy shaping as an enhancement of classical passivity-based control methods. A promising feature of passivity theory, alongside stability, has traditionally claimed to be intuitive performance tuning along the execution of a given task. However, a systematic approach for adjusting performance within a passive control framework has yet to be developed, as each method relies on few and problem-specific practical insights. Here, we cast the classic energy-shaping control design process in an optimal control framework; once a task-dependent performance metric is defined, an optimal solution is systematically obtained through an iterative procedure relying on neural networks and gradient-based optimization. The proposed method is validated on state-regulation tasks. © 2022 Society for Industrial and Applied Mathematics.-
dc.languageEnglish-
dc.publisherSIAM PUBLICATIONS-
dc.titleOptimal Energy Shaping via Neural Approximators-
dc.typeArticle-
dc.identifier.wosid000913566000005-
dc.identifier.scopusid2-s2.0-85138449970-
dc.type.rimsART-
dc.citation.volume21-
dc.citation.issue3-
dc.citation.beginningpage2126-
dc.citation.endingpage2147-
dc.citation.publicationnameSIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS-
dc.identifier.doi10.1137/21M1414279-
dc.contributor.localauthorCalifano, Federico-
dc.contributor.nonIdAuthorMassaroli, Stefano-
dc.contributor.nonIdAuthorPoli, Michael-
dc.contributor.nonIdAuthorPark, Jinkyoo-
dc.contributor.nonIdAuthorYamashita, Atsushi-
dc.contributor.nonIdAuthorAsama, Hajime-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorneural approximators-
dc.subject.keywordAuthorpassivity-based control-
dc.subject.keywordAuthorport-Hamiltonian systems-
dc.subject.keywordAuthorstability-
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