DC Field | Value | Language |
---|---|---|
dc.contributor.author | Massaroli, Stefano | ko |
dc.contributor.author | Poli, Michael | ko |
dc.contributor.author | Califano, Federico | ko |
dc.contributor.author | Park, Jinkyoo | ko |
dc.contributor.author | Yamashita, Atsushi | ko |
dc.contributor.author | Asama, Hajime | ko |
dc.date.accessioned | 2022-12-23T02:00:36Z | - |
dc.date.available | 2022-12-23T02:00:36Z | - |
dc.date.created | 2022-12-23 | - |
dc.date.created | 2022-12-23 | - |
dc.date.created | 2022-12-23 | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS, v.21, no.3, pp.2126 - 2147 | - |
dc.identifier.issn | 1536-0040 | - |
dc.identifier.uri | http://hdl.handle.net/10203/303609 | - |
dc.description.abstract | We 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.language | English | - |
dc.publisher | SIAM PUBLICATIONS | - |
dc.title | Optimal Energy Shaping via Neural Approximators | - |
dc.type | Article | - |
dc.identifier.wosid | 000913566000005 | - |
dc.identifier.scopusid | 2-s2.0-85138449970 | - |
dc.type.rims | ART | - |
dc.citation.volume | 21 | - |
dc.citation.issue | 3 | - |
dc.citation.beginningpage | 2126 | - |
dc.citation.endingpage | 2147 | - |
dc.citation.publicationname | SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS | - |
dc.identifier.doi | 10.1137/21M1414279 | - |
dc.contributor.localauthor | Califano, Federico | - |
dc.contributor.nonIdAuthor | Massaroli, Stefano | - |
dc.contributor.nonIdAuthor | Poli, Michael | - |
dc.contributor.nonIdAuthor | Park, Jinkyoo | - |
dc.contributor.nonIdAuthor | Yamashita, Atsushi | - |
dc.contributor.nonIdAuthor | Asama, Hajime | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | neural approximators | - |
dc.subject.keywordAuthor | passivity-based control | - |
dc.subject.keywordAuthor | port-Hamiltonian systems | - |
dc.subject.keywordAuthor | stability | - |
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