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

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Latifi, Kourosh
dc.contributor.author Kopitca, Artur
dc.contributor.author Zhou, Quan
dc.date.accessioned 2020-02-28T09:29:31Z
dc.date.available 2020-02-28T09:29:31Z
dc.date.issued 2020-01-24
dc.identifier.citation Latifi , K , Kopitca , A & Zhou , Q 2020 , ' Model-Free Control for Dynamic-Field Acoustic Manipulation Using Reinforcement Learning ' , IEEE Access , vol. 8 , pp. 20597-20606 . https://doi.org/10.1109/ACCESS.2020.2969277 en
dc.identifier.issn 2169-3536
dc.identifier.other PURE UUID: 37bc221e-e0a2-4788-88aa-5ec2c8cb57f2
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/modelfree-control-for-dynamicfield-acoustic-manipulation-using-reinforcement-learning(37bc221e-e0a2-4788-88aa-5ec2c8cb57f2).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/40756289/Model_Free_Control_for_Dynamic_Field_Acoustic_Manipulation_Using_Reinforcement_Learning.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/43247
dc.description.abstract Dynamic-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.format.extent 10
dc.format.extent 20597-20606
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartofseries IEEE Access en
dc.relation.ispartofseries Volume 8 en
dc.rights openAccess en
dc.subject.other 213 Electronic, automation and communications engineering, electronics en
dc.subject.other 2 Engineering and technology en
dc.subject.other Automation and systems technology en
dc.title Model-Free Control for Dynamic-Field Acoustic Manipulation Using Reinforcement Learning en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Robotic Instruments
dc.contributor.department Department of Electrical Engineering and Automation
dc.contributor.department Department of Electrical Engineering and Automation en
dc.subject.keyword Acoustic manipulation
dc.subject.keyword Chladni plate
dc.subject.keyword Dynamic-filed acoustic device
dc.subject.keyword Model-free control
dc.subject.keyword Real-time control
dc.subject.keyword Reinforced learning
dc.subject.keyword 213 Electronic, automation and communications engineering, electronics
dc.subject.keyword 2 Engineering and technology
dc.subject.keyword Automation and systems technology
dc.identifier.urn URN:NBN:fi:aalto-202002282296
dc.identifier.doi 10.1109/ACCESS.2020.2969277
dc.type.version publishedVersion


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