{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108556"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108556","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Unknown input and state estimation for linear discrete-time stochastic systems in the presence of constraints","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Wan, Wenbin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Applied Mathematics","degree_department":null,"school":null,"contributors":["Hovakimyan, Naira"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-05-13","date_published":"2020-05-13","updated_at":"2026-07-22T22:24:48Z","subjects":["Stochastic systems","Estimation"],"languages":["en"],"rights":["Copyright 2020 Wenbin Wan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108556","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hovakimyan, Naira"]},{"key":"dc:creator","label":"Author","values":["Wan, Wenbin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-05-13","2020-10-07T22:07:09Z","2022-10-07T22:44:53Z","2020-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Applied Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Stochastic systems","Estimation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Wenbin Wan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108556"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01","The student, Wenbin Wan, accepted the attached license on 2020-05-11 at 10:50.","The student, Wenbin Wan, submitted this Thesis for approval on 2020-05-11 at 10:53.","This Thesis was approved for publication on 2020-05-13 at 07:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15308 on 2020-10-02 at 15:30:37","Made available in DSpace on 2020-10-07T22:07:09Z (GMT). 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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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01","The student, Wenbin Wan, accepted the attached license on 2020-05-11 at 10:50.","The student, Wenbin Wan, submitted this Thesis for approval on 2020-05-11 at 10:53.","This Thesis was approved for publication on 2020-05-13 at 07:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15308 on 2020-10-02 at 15:30:37","Made available in DSpace on 2020-10-07T22:07:09Z (GMT). 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