Unknown input and state estimation for linear discrete-time stochastic systems in the presence of constraints
Wan, Wenbin
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https://hdl.handle.net/2142/108556
Description
Title
Unknown input and state estimation for linear discrete-time stochastic systems in the presence of constraints
Author(s)
Wan, Wenbin
Issue Date
2020-05-13
Director of Research (if dissertation) or Advisor (if thesis)
Hovakimyan, Naira
Department of Study
Mathematics
Discipline
Applied Mathematics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Stochastic systems
Estimation
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.
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