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dc.contributor.authorHosseinloo, Ashkan Haji
dc.contributor.authorVu, Thanh Long
dc.contributor.authorTuritsyn, Konstantin
dc.date.accessioned2024-05-30T20:54:29Z
dc.date.available2024-05-30T20:54:29Z
dc.date.issued2015-12
dc.identifier.urihttps://hdl.handle.net/1721.1/155145
dc.description2015 54th IEEE Conference on Decision and Control (CDC), Osaka Japanen_US
dc.description.abstractEase of miniaturization and minimal maintenance are among the advantages for replacing conventional batteries with vibratory energy harvesters in a wide of range of disciplines and applications, from wireless communication sensors to medical implants. However, the current harvesters do not extract energy from the ambient vibrations in a very efficient and robust fashion, and hence, there need to be more optimal harvesting approaches. In this paper, we introduce a generic architecture for vibration energy harvesting and delineate the key challenges in the field. Then, we formulate an optimal control problem to maximize the harvested energy. Though possessing similar structure to that of the standard LQG problem, this optimal control problem is inherently different from the LQG problem and poses theoretical challenges to control community. As the first step, we simplify it to a tractable problem of optimizing control gains for a linear system subjected to Gaussian white noise excitation, and show that this optimal problem has non-trivial optimal solutions in both time and frequency domains.en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/cdc.2015.7403063en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAuthoren_US
dc.titleOptimal control strategies for efficient energy harvesting from ambient vibrationen_US
dc.typeArticleen_US
dc.identifier.citationA. H. Hosseinloo, T. L. Vu and K. Turitsyn, "Optimal control strategies for efficient energy harvesting from ambient vibration," 2015 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, 2015, pp. 5391-5396.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineering
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2024-05-30T20:46:59Z
dspace.orderedauthorsHosseinloo, AH; Vu, TL; Turitsyn, Ken_US
dspace.date.submission2024-05-30T20:47:00Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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