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A Study on Constrained State Estimators


Abstract This study focuses on state estimation of nonlinear discrete time systems with constraints. Physical processes have inherent in them, constraints on inputs, outputs, states and disturbances. These constraints can provide additional information to the estimator in estimating states from the measured output. Recursive filters such as Kalman Filters or Extended Kalman Filters are commonly used in state estimation; however, they do not allow inclusion of constraints in their formulation. On the other hand, computational complexity of full information estimation (using all measurements) grows with iteration and becomes intractable. One way of formulating the recursive state estimation problem with constraints is the Moving Horizon Estimation (MH... (more)
Created Date 2013
Contributor Joshi, Rakesh (Author) / Tsakalis, Konstantinos (Advisor) / Rodriguez, Armando (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Subject Engineering / kalman filter / mhe / state estimation
Type Masters Thesis
Extent 68 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Electrical Engineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis