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Title:Unknown input and state estimation for linear discrete-time stochastic systems in the presence of constraints
Author(s):Wan, Wenbin
Advisor(s):Hovakimyan, Naira
Department / Program:Mathematics
Discipline:Applied Mathematics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Stochastic systems
Abstract:This thesis presents an unknown input and state estimation algorithm for linear discrete-time stochastic systems with inequality constraints on the inputs and states. The proposed algorithm consists of optimal Bayesian estimation and information aggregation. The optimal estimation provides minimum-variance unbiased (MVU) estimates, and then they are projected onto the constrained space in the information aggregation step. It is shown that the estimation errors and their covariances from the proposed algorithm are strictly less than those from the unconstrained algorithm when projected. Moreover, the expected state estimation errors of the proposed estimation algorithm are proved to be practically exponentially stable.
Issue Date:2020-05-13
Rights Information:Copyright 2020 Wenbin Wan
Date Available in IDEALS:2020-10-07
Date Deposited:2020-08

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