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

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/13951

Title: Estimation of the Longitudinal and Lateral Velocities of a Vehicle using Extended Kalman Filters
Author: Alvarez, Juan Camilo
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.
Type: Thesis
URI: http://hdl.handle.net/1853/13951
Date: 2006-11-20
Publisher: Georgia Institute of Technology
Subject: Estimation velocity
Vehicle
Extended Kalman filters
Kalman filtering
Motion control devices Design and construction
Department: Electrical and Computer Engineering
Advisor: Committee Chair: Taylor, David; Committee Member: Egerstedt, Magnus; Committee Member: Verriest, Erik
Degree: M.S.

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