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AuthorAbdeljaber, Osama
AuthorAvci, Onur
AuthorInman, Daniel J.
Available date2021-04-29T08:10:43Z
Publication Date2016
Publication NameJournal of Sound and Vibration
ResourceScopus
ISSN0022460X
URIhttp://dx.doi.org/10.1016/j.jsv.2015.10.029
URIhttp://hdl.handle.net/10576/18366
AbstractThe study presented in this paper introduces a new intelligent methodology to mitigate the vibration response of flexible cantilever plates. The use of the piezoelectric sensor/actuator pairs for active control of plates is discussed. An intelligent neural network based controller is designed to control the optimal voltage applied on the piezoelectric patches. The control technique utilizes a neurocontroller along with a Kalman Filter to compute the appropriate actuator command. The neurocontroller is trained based on an algorithm that incorporates a set of emulator neural networks which are also trained to predict the future response of the cantilever plate. Then, the neurocontroller is evaluated by comparing the uncontrolled and controlled responses under several types of dynamic excitations. It is observed that the neurocontroller reduced the vibration response of the flexible cantilever plate significantly; the results demonstrated the success and robustness of the neurocontroller independent of the type and distribution of the excitation force.
SponsorThe financial support for this research was provided by Qatar National Research Fund , QNRF (a member of Qatar Foundation) via the National Priorities Research Program (NPRP) , Project no. NPRP 6-526-2-218 . The statements made herein are solely the responsibility of the authors.
Languageen
PublisherAcademic Press
SubjectNanocantilevers
Neural networks
Piezoelectric devices
Piezoelectric materials
Piezoelectricity
Active vibration controls
Control techniques
Controlled response
Dynamic excitations
Network-based controllers
Piezoelectric patch
Piezoelectric sensors
Vibration response
Vibrations (mechanical)
TitleActive vibration control of flexible cantilever plates using piezoelectric materials and artificial neural networks
TypeArticle
Pagination33-53
Volume Number363
dc.accessType Abstract Only


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