{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/114106"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/114106","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Flight simulation and hardware implementation of deep model predictive control","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-12-01","abstract_html":"Submission published under a 24 month embargo labeled &#x27;Closed Access&#x27;, the embargo will last until 2023-12-01","abstract_has_math":false,"creators":["Gowan, Garrett"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Chowdhary, Girish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-04-29T21:58:40Z","date_published":"2022-04-29T21:58:40Z","updated_at":"2026-07-22T22:24:54Z","subjects":["Engineering"],"languages":["en","eng"],"rights":["Copyright 2021 Garrett Gowan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/114106","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish"]},{"key":"dc:creator","label":"Author","values":["Gowan, Garrett"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-04-29T21:58:40Z","2024-04-29T21:58:46Z","2021-12","2021-12-09"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"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":["Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Garrett Gowan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/114106"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-12-01","The student, Garrett Gowan, accepted the attached license on 2021-12-07 at 11:17.","The student, Garrett Gowan, submitted this Thesis for approval on 2021-12-08 at 12:12.","This Thesis was approved for publication on 2021-12-09 at 13:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17399 on 2022-04-29 at 16:10:39","Made available in DSpace on 2022-04-29T21:58:40Z (GMT). No. of bitstreams: 3 GOWAN-THESIS-2021.pdf: 5040272 bytes, checksum: 849a5dc2e72b858d5bf77df83a19a272 (MD5) GarrettGowan_MastersThesis.zip: 6994295 bytes, checksum: eb083e85766910f6fd6ea99ba05ff916 (MD5) LICENSE.txt: 4210 bytes, checksum: dd09ae5a801d46776342b338892aaea4 (MD5) Previous issue date: 2021-12-09","Embargo set by: Seth Robbins for item 123471 Lift date: 2024-04-29T21:58:46Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited","This thesis presents the flight simulation and hardware implementation of Deep Model Predictive Control (DMPC) on an experimental setup, which consists of a quadcopter and motion capture system. DMPC aims to adapt abrupt state-dependent matched uncertainties arising due to faults, collects training data for a deep neural network (DNN) to learn slowly varying features, and ensures safety during the learning phase. Training of DNN to learn features is carried out on a parallel machine, while the actual system is controlled by a tube MPC and an adaptive mechanism with fixed features. Under certain verifiable technical conditions, DMPC ensures the asymptotic stability of closed-loop states. Through simulations presented in this thesis, it is shown that DMPC can outperform other control architectures when utilizing an affine model with additive nonlinear disturbance to the control input, and is able to guarantee stability while avoiding unwanted behavior in early learning phases while having long-term learning capabilities. This study demonstrates that DMPC is a powerful and safe control architecture for nonlinear systems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Flight simulation and hardware implementation of deep model predictive control"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish"],"dc:creator":["Gowan, Garrett"],"dc:date":["2022-04-29T21:58:40Z","2024-04-29T21:58:46Z","2021-12","2021-12-09"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-12-01","The student, Garrett Gowan, accepted the attached license on 2021-12-07 at 11:17.","The student, Garrett Gowan, submitted this Thesis for approval on 2021-12-08 at 12:12.","This Thesis was approved for publication on 2021-12-09 at 13:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17399 on 2022-04-29 at 16:10:39","Made available in DSpace on 2022-04-29T21:58:40Z (GMT). No. of bitstreams: 3 GOWAN-THESIS-2021.pdf: 5040272 bytes, checksum: 849a5dc2e72b858d5bf77df83a19a272 (MD5) GarrettGowan_MastersThesis.zip: 6994295 bytes, checksum: eb083e85766910f6fd6ea99ba05ff916 (MD5) LICENSE.txt: 4210 bytes, checksum: dd09ae5a801d46776342b338892aaea4 (MD5) Previous issue date: 2021-12-09","Embargo set by: Seth Robbins for item 123471 Lift date: 2024-04-29T21:58:46Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited","This thesis presents the flight simulation and hardware implementation of Deep Model Predictive Control (DMPC) on an experimental setup, which consists of a quadcopter and motion capture system. DMPC aims to adapt abrupt state-dependent matched uncertainties arising due to faults, collects training data for a deep neural network (DNN) to learn slowly varying features, and ensures safety during the learning phase. Training of DNN to learn features is carried out on a parallel machine, while the actual system is controlled by a tube MPC and an adaptive mechanism with fixed features. Under certain verifiable technical conditions, DMPC ensures the asymptotic stability of closed-loop states. Through simulations presented in this thesis, it is shown that DMPC can outperform other control architectures when utilizing an affine model with additive nonlinear disturbance to the control input, and is able to guarantee stability while avoiding unwanted behavior in early learning phases while having long-term learning capabilities. This study demonstrates that DMPC is a powerful and safe control architecture for nonlinear systems."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/114106"],"dc:language":["en","eng"],"dc:rights":["Copyright 2021 Garrett Gowan"],"dc:subject":["Engineering"],"dc:title":["Flight simulation and hardware implementation of deep model predictive control"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}