Unscented kalman filter for the identification of passive control devices

Show simple item record

dc.contributor.author Ceravolo, Rosario
dc.contributor.author De Stefano, Alessandro
dc.contributor.author Matta, Emiliano
dc.contributor.author Quattrone, Antonino
dc.contributor.author Zanotti Fragonara, Luca
dc.date.accessioned 2017-08-01T09:07:31Z
dc.date.available 2017-08-01T09:07:31Z
dc.date.issued 2013-12-31
dc.identifier.citation Rosario Ceravolo, Alessandro De Stefano, Emiliano Matta, Antonino Quattrone and Luca Zanotti Fragonara. Unscented kalman filter for the identification of passive control devices. Proceedings of the Fifth International Conference on Structural Engineering and Computation, 2-4 September 2013, Cape Town, South Africa. en_UK
dc.identifier.isbn 9781138000612
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/12255
dc.description.abstract The Unscented Kalman Filter (UKF) is a technique which allows dealing with nonlinear systems and it is able to handle any type of non-linearity. In detail, differently from Extended Kalman Filter (EKF), UKF does not require the computation of the Jacobian of the non-linear function. Estimation of parameters through the UKF approach is an indirect procedure, consisting of transforming the parameter estimation problem into a state estimation problem. This is done by augmenting the system state vector by artificially defining the unknown parameters as additional state variables. In the present study the UKF is proposed to the purpose of the nonlinear identification of rolling-pendulum tuned vibration absorbers. en_UK
dc.language.iso en en_UK
dc.publisher Taylor & Francis en_UK
dc.rights ©2013 Taylor and Francis. This is the Author Accepted Manuscript. Please refer to any applicable publisher terms of use.
dc.title Unscented kalman filter for the identification of passive control devices en_UK
dc.type Conference paper en_UK


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search CERES


Browse

My Account

Statistics