Unknown input and state estimation for linear discrete-time stochastic systems in the presence of constraints

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Applied Mathematics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Wan, Wenbin

Contributors dc:contributor

• Hovakimyan, Naira

• Stochastic systems
• Estimation

Statement dc:rights

• Copyright 2020 Wenbin Wan

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108556

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108556