Extended Kalman Filter Simulink Model for Nonlinear System Modeling

Graduate Thesis Or Dissertation

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Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/4m90dz41d

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Attribute Name	Values
Creator	So, Ratanak
Abstract	The objective of the work presented herein is the development of the extended Kalman filter for nonlinear system modeling. A standard Kalman filter is a well-known filter for estimating the state of a system, assuming the system is linear and it has a Gaussian distribution in its noise. In reality, linear systems don't really exist. As a result, the standard Kalman filter is inadequate for modeling most systems. This need could be addressed by changing the standard Kalman filter to work in a nonlinear system. The extended Kalman filter Simulink model proposed in this work allows modeling in nonlinear systems through local linearization. This approach is validated by accurately estimating the nonlinear system behavior for a permanent magnet synchronous motor.
License	All rights reserved
Resource Type	Masters Thesis
Date Available	2015-06-23T23:10:21+00:00
Date Issued	2015-05-15
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Electrical and Computer Engineering
Degree Grantor	Oregon State University
Commencement Year	2015
Advisor	Brekken, Ted K. A.
Committee Member	Zhang, Julia Sanchez, Eduardo Jouanne, Annette Nishihara, Janet
Academic Affiliation	Electrical Engineering and Computer Science
Non-Academic Affiliation	Oregon State University. Graduate School
Subject	Kalman filtering Nonlinear systems
Rights Statement	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]
Replaces	http://hdl.handle.net/1957/56248

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