Title:
Estimation of the Longitudinal and Lateral Velocities of a Vehicle using Extended Kalman Filters

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dc.contributor.advisor Taylor, David G.
dc.contributor.author Alvarez, Juan Camilo en_US
dc.contributor.committeeMember Egerstedt, Magnus
dc.contributor.committeeMember Verriest, Erik
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2007-03-27T18:03:05Z
dc.date.available 2007-03-27T18:03:05Z
dc.date.issued 2006-11-20 en_US
dc.description.abstract Vehicle motion and tire forces have been estimated using extended Kalman filters for many years. The use of extended Kalman filters is primarily motivated by the simultaneous presence of nonlinear dynamics and sensor noise. Two versions of extended Kalman filters are employed in this thesis: one using a deterministic tire-force model and the other using a stochastic tire-force model. Previous literature has focused on linear stochastic tire-force models and on linear deterministic tire-force models. However, it is well known that there exists a nonlinear relationship between slip variables and tire-force variables. For this reason, it is suitable to use a nonlinear deterministic tire-force model for the extended Kalman filter, and this is the novel aspect at this work. The objective of this research is to show the improvement of the extended Kalman filter using a nonlinear deterministic tire-force model in comparison to linear stochastic tire-force model. The simulation model is a seven degree-of-freedom bicycle model that includes vertical suspension dynamics but neglects the roll motion. A comparison between the linear stochastic tire-force model and the nonlinear deterministic tire-force model confirms the expected results. Simulation studies are performed on some illustrative examples obtaining good tracking performance. en_US
dc.description.degree M.S. en_US
dc.format.extent 477205 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/13951
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Estimation velocity en_US
dc.subject Vehicle en_US
dc.subject Extended Kalman filters en_US
dc.subject.lcsh Kalman filtering en_US
dc.subject.lcsh Motion control devices Design and construction en_US
dc.title Estimation of the Longitudinal and Lateral Velocities of a Vehicle using Extended Kalman Filters en_US
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Taylor, David G.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication a6615de9-0526-43fd-84a6-b2185a733191
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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