{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129620"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129620","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Supervisory control with online learning for stabilization and near-optimal performance of time-varying linear systems","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Roy, Dhritiman"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Li, Yingying"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-08","date_published":"2025-05-08","updated_at":"2026-07-22T22:25:05Z","subjects":["Online Learning","Multi-armed Bandit","Adaptive Control","Pontryagin’s Maximum Principle"],"languages":["en","eng"],"rights":["Copyright 2025 Dhritiman Roy"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129620","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Li, Yingying"]},{"key":"dc:creator","label":"Author","values":["Roy, Dhritiman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-08","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial 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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Online Learning","Multi-armed Bandit","Adaptive Control","Pontryagin’s Maximum Principle"]}]},{"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 2025 Dhritiman Roy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129620"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, Dhritiman Roy, accepted the attached license on 2025-05-01 at 13:19.","The student, Dhritiman Roy, submitted this Thesis for approval on 2025-05-01 at 13:31.","This Thesis was approved for publication on 2025-05-08 at 16:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22152 on 2025-10-19 at 19:16:58","Model-based control methods are widely used in robotics because they use system equations to compute efficient control actions. However, these methods often struggle in real-world situations where the system model is not perfect or where there are unexpected disturbances. In addition, solving nonlinear optimization problems in real time can be too slow or too demanding for systems with limited onboard computing power. To address these challenges, this study proposes a hybrid control approach that combines classical control, optimal planning and online learning. The system we focus on is a 2D quadrotor, modeled as a six-dimensional system controlled using force and torque inputs. At the lower level, we use three different types of controllers: a basic Proportional-Derivative (PD) controller, a trajectory planner using nonlinear programming (NLP), and a control law based on Pontryagin’s Maximum Principle (PMP), which we implement using PyTorch. At the higher level, we add a Multi-Armed Bandit (MAB) layer using the EXP3 algorithm. This layer learns over time which controller performs best based on feedback like tracking error and energy usage. It allows the system to switch between controllers depending on how well they are working at each moment. Our results show that this combination of planning and learning can make the system more reliable and adaptive, even in uncertain environments. While we apply this to a quadrotor, the same idea can be used for many other types of robotic systems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Supervisory control with online learning for stabilization and near-optimal performance of time-varying linear systems"]}]}],"canonical_facts":{"dc:contributor":["Li, Yingying"],"dc:creator":["Roy, Dhritiman"],"dc:date":["2025-05-08","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, Dhritiman Roy, accepted the attached license on 2025-05-01 at 13:19.","The student, Dhritiman Roy, submitted this Thesis for approval on 2025-05-01 at 13:31.","This Thesis was approved for publication on 2025-05-08 at 16:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22152 on 2025-10-19 at 19:16:58","Model-based control methods are widely used in robotics because they use system equations to compute efficient control actions. However, these methods often struggle in real-world situations where the system model is not perfect or where there are unexpected disturbances. In addition, solving nonlinear optimization problems in real time can be too slow or too demanding for systems with limited onboard computing power. To address these challenges, this study proposes a hybrid control approach that combines classical control, optimal planning and online learning. The system we focus on is a 2D quadrotor, modeled as a six-dimensional system controlled using force and torque inputs. At the lower level, we use three different types of controllers: a basic Proportional-Derivative (PD) controller, a trajectory planner using nonlinear programming (NLP), and a control law based on Pontryagin’s Maximum Principle (PMP), which we implement using PyTorch. At the higher level, we add a Multi-Armed Bandit (MAB) layer using the EXP3 algorithm. This layer learns over time which controller performs best based on feedback like tracking error and energy usage. It allows the system to switch between controllers depending on how well they are working at each moment. Our results show that this combination of planning and learning can make the system more reliable and adaptive, even in uncertain environments. While we apply this to a quadrotor, the same idea can be used for many other types of robotic systems."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129620"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Dhritiman Roy"],"dc:subject":["Online Learning","Multi-armed Bandit","Adaptive Control","Pontryagin’s Maximum Principle"],"dc:title":["Supervisory control with online learning for stabilization and near-optimal performance of time-varying linear systems"],"dc:type":["text"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}