Model-Free Control for Dynamic-Field Acoustic Manipulation Using Reinforcement Learning

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorLatifi, Kouroshen_US
dc.contributor.authorKopitca, Arturen_US
dc.contributor.authorZhou, Quanen_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.groupauthorRobotic Instrumentsen
dc.date.accessioned2020-02-28T09:29:31Z
dc.date.available2020-02-28T09:29:31Z
dc.date.issued2020-01-24en_US
dc.description.abstractDynamic-field acoustic manipulation techniques benefit numerous applications in microsystem assembly, pattern formation, biological research, tissue engineering, and lab-on-a-chip. These techniques generally rely on a theoretical dynamic model of particle motion in the acoustic field. Accordingly, success of the manipulation task highly depends on the accuracy of the employed dynamic model. However, modelling such dynamic behavior is a great challenge in more advanced acoustic manipulation devices and requires significant simplifications. Here, we introduce a model-free control method based on reinforcement learning for highly-dynamic acoustic manipulation devices. In our method, the controller does not need a prior knowledge of the acoustic field and learns the optimal control policy for each manipulation task by merely interacting with the acoustic field. As a proof-of-concept, we apply our method to a classic acoustic manipulation device, a Chladni plate consisting of a centrally-actuated vibrating plate. By employing the controller, we demonstrate successful manipulation of single and multiple particles towards target locations on the plate surface. The model-free control method is not limited to the Chladni plate and can be potentially applied to a broad range of acoustic manipulation devices as well as other forms of field-based micromanipulation systems, where accurate theoretical modelling of the field is challenging.en
dc.description.versionPeer revieweden
dc.format.extent10
dc.format.extent20597-20606
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationLatifi, K, Kopitca, A & Zhou, Q 2020, ' Model-Free Control for Dynamic-Field Acoustic Manipulation Using Reinforcement Learning ', IEEE Access, vol. 8, 8968432, pp. 20597-20606 . https://doi.org/10.1109/ACCESS.2020.2969277en
dc.identifier.doi10.1109/ACCESS.2020.2969277en_US
dc.identifier.issn2169-3536
dc.identifier.otherPURE UUID: 37bc221e-e0a2-4788-88aa-5ec2c8cb57f2en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/37bc221e-e0a2-4788-88aa-5ec2c8cb57f2en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85079759130&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/40756289/Model_Free_Control_for_Dynamic_Field_Acoustic_Manipulation_Using_Reinforcement_Learning.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/43247
dc.identifier.urnURN:NBN:fi:aalto-202002282296
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofseriesIEEE Accessen
dc.relation.ispartofseriesVolume 8en
dc.rightsopenAccessen
dc.subject.keywordAcoustic manipulationen_US
dc.subject.keywordChladni plateen_US
dc.subject.keywordDynamic-filed acoustic deviceen_US
dc.subject.keywordModel-free controlen_US
dc.subject.keywordReal-time controlen_US
dc.subject.keywordReinforced learningen_US
dc.titleModel-Free Control for Dynamic-Field Acoustic Manipulation Using Reinforcement Learningen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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