{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124381"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124381","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Design and flight evaluation of deep model predictive control","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["McCann, Dennis"],"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":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Deep Model Predictive Control","Deep Learning","Adaptive Control","Online Learning","Model Predictive Control","Quadrotors"],"languages":["en","eng"],"rights":["Copyright 2024 Dennis McCann"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124381","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":["McCann, Dennis"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-30"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["Deep Model Predictive Control","Deep Learning","Adaptive Control","Online Learning","Model Predictive Control","Quadrotors"]}]},{"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 2024 Dennis McCann"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124381"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Dennis McCann, accepted the attached license on 2024-04-23 at 13:20.","The student, Dennis McCann, submitted this Thesis for approval on 2024-04-23 at 13:33.","This Thesis was approved for publication on 2024-04-30 at 16:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20580 on 2024-09-16 at 00:36:01","As autonomous systems are increasingly employed in our society, the necessity for robust and adaptive control mechanisms to ensure their safety and effectiveness has become paramount. This thesis focuses on the implementation and evaluation of Deep Model Predictive Control, an adaptive control algorithm designed to manage nonlinear systems experiencing matched and bounded state-dependent uncertainties or disturbances. Deep Model Predictive Control stands out for its ability to make real-time adjustments to disturbances, utilizing a deep learning based adaptive architecture, while satisfying system constraints and stability guarantees. Through a comprehensive series of experiments escalating in complexity—from numerical simulations to flight tests on actual hardware—this research aims to validate the efficacy and applicability of Deep Model Predictive Control in enhancing the autonomy, reliability, and safety of these systems. By systematically increasing the fidelity of our experimental evaluations, not only the theoretical viability of Deep Model Predictive Control is assessed, but also the practical viability, by implementing the algorithm to control the physical Crazyflie 2.0 quadrotor. This work endeavors to bridge theoretical adaptive control strategy of Deep Model Predictive Control with real-world applications, marking a step forward in the deployment of adaptive controllers that harness deep learning for the estimation and mitigation of uncertainty."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Design and flight evaluation of deep model predictive control"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish"],"dc:creator":["McCann, Dennis"],"dc:date":["2024-05","2024-04-30"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Dennis McCann, accepted the attached license on 2024-04-23 at 13:20.","The student, Dennis McCann, submitted this Thesis for approval on 2024-04-23 at 13:33.","This Thesis was approved for publication on 2024-04-30 at 16:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20580 on 2024-09-16 at 00:36:01","As autonomous systems are increasingly employed in our society, the necessity for robust and adaptive control mechanisms to ensure their safety and effectiveness has become paramount. This thesis focuses on the implementation and evaluation of Deep Model Predictive Control, an adaptive control algorithm designed to manage nonlinear systems experiencing matched and bounded state-dependent uncertainties or disturbances. Deep Model Predictive Control stands out for its ability to make real-time adjustments to disturbances, utilizing a deep learning based adaptive architecture, while satisfying system constraints and stability guarantees. Through a comprehensive series of experiments escalating in complexity—from numerical simulations to flight tests on actual hardware—this research aims to validate the efficacy and applicability of Deep Model Predictive Control in enhancing the autonomy, reliability, and safety of these systems. By systematically increasing the fidelity of our experimental evaluations, not only the theoretical viability of Deep Model Predictive Control is assessed, but also the practical viability, by implementing the algorithm to control the physical Crazyflie 2.0 quadrotor. This work endeavors to bridge theoretical adaptive control strategy of Deep Model Predictive Control with real-world applications, marking a step forward in the deployment of adaptive controllers that harness deep learning for the estimation and mitigation of uncertainty."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124381"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Dennis McCann"],"dc:subject":["Deep Model Predictive Control","Deep Learning","Adaptive Control","Online Learning","Model Predictive Control","Quadrotors"],"dc:title":["Design and flight evaluation of deep model predictive control"],"dc:type":["text"],"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":"2026-07-22T22:25:00Z"}