{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106283"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106283","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Modular modeling and control of a hybrid unmanned aerial vehicle’s powertrain","abstract":"The emerging trend of vehicle electrification is revolutionizing the transportation industry by replacing traditional mechanical and hydraulic components with higher performing, more reliable, and more efficient electrical components. However, the introduction of a complex electrical network onboard mobile systems poses significant challenges for control design engineers. The most notable challenge is the coordination of multi-domain and multi-timescale system dynamics. This thesis seeks to address the challenge of coordination between the slow battery state of charge dynamic and faster electro-mechanical dynamics for a hybrid unmanned aerial vehicle. The graph-based modeling framework for multi-domain systems is leveraged to capture the interactions between relevant energy domains. Additionally, the modularity and scalability of this modeling approach is used to develop a dynamic model for a hybrid unmanned aerial vehicle. The system model facilities the design and development of three control architectures of varying complexity. A baseline controller is developed for sake of comparison. A battery state of charge bounding algorithm in integrated into a centralized model predictive controller to provide system coordination across timescales. Lastly, an alternative model predictive hierarchical controller is designed to provide real-time planning of the slow battery state of charge dynamics. The proposed models and controllers are experimentally validated on a novel hybrid electric UAV powertrain testbed. The controllers are evaluated on three core figures of merit: performance, reliability, and efficiency. Both simulation and experimental results show that the advanced controllers outperform the baseline in all figures of merit with a 9-12.5% reduction in fuel usage.","abstract_html":"The emerging trend of vehicle electrification is revolutionizing the transportation industry by replacing traditional mechanical and hydraulic components with higher performing, more reliable, and more efficient electrical components. However, the introduction of a complex electrical network onboard mobile systems poses significant challenges for control design engineers. The most notable challenge is the coordination of multi-domain and multi-timescale system dynamics. This thesis seeks to address the challenge of coordination between the slow battery state of charge dynamic and faster electro-mechanical dynamics for a hybrid unmanned aerial vehicle. The graph-based modeling framework for multi-domain systems is leveraged to capture the interactions between relevant energy domains. Additionally, the modularity and scalability of this modeling approach is used to develop a dynamic model for a hybrid unmanned aerial vehicle. The system model facilities the design and development of three control architectures of varying complexity. A baseline controller is developed for sake of comparison. A battery state of charge bounding algorithm in integrated into a centralized model predictive controller to provide system coordination across timescales. Lastly, an alternative model predictive hierarchical controller is designed to provide real-time planning of the slow battery state of charge dynamics. The proposed models and controllers are experimentally validated on a novel hybrid electric UAV powertrain testbed. The controllers are evaluated on three core figures of merit: performance, reliability, and efficiency. Both simulation and experimental results show that the advanced controllers outperform the baseline in all figures of merit with a 9-12.5% reduction in fuel usage.","abstract_has_math":false,"creators":["Aksland, Christopher T"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Alleyne, Andrew G"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T22:03:25Z","date_published":"2020-03-02T22:03:25Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Physics-Based Modeling, Control Systems, Mobile Energy Systems, Model Predictive Control, Hierarchical Control, Experimental Validation"],"languages":["en"],"rights":["Copyright Christopher Thomas Aksland 2019"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106283","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alleyne, Andrew G"]},{"key":"dc:creator","label":"Author","values":["Aksland, Christopher T"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T22:03:25Z","2019-12-12","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical 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":["Physics-Based Modeling, Control Systems, Mobile Energy Systems, Model Predictive Control, Hierarchical Control, Experimental Validation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright Christopher Thomas Aksland 2019"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106283"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The emerging trend of vehicle electrification is revolutionizing the transportation industry by replacing traditional mechanical and hydraulic components with higher performing, more reliable, and more efficient electrical components. However, the introduction of a complex electrical network onboard mobile systems poses significant challenges for control design engineers. The most notable challenge is the coordination of multi-domain and multi-timescale system dynamics. This thesis seeks to address the challenge of coordination between the slow battery state of charge dynamic and faster electro-mechanical dynamics for a hybrid unmanned aerial vehicle. The graph-based modeling framework for multi-domain systems is leveraged to capture the interactions between relevant energy domains. Additionally, the modularity and scalability of this modeling approach is used to develop a dynamic model for a hybrid unmanned aerial vehicle. The system model facilities the design and development of three control architectures of varying complexity. A baseline controller is developed for sake of comparison. A battery state of charge bounding algorithm in integrated into a centralized model predictive controller to provide system coordination across timescales. Lastly, an alternative model predictive hierarchical controller is designed to provide real-time planning of the slow battery state of charge dynamics. The proposed models and controllers are experimentally validated on a novel hybrid electric UAV powertrain testbed. The controllers are evaluated on three core figures of merit: performance, reliability, and efficiency. Both simulation and experimental results show that the advanced controllers outperform the baseline in all figures of merit with a 9-12.5% reduction in fuel usage.","Submission original under an indefinite embargo labeled 'Open Access'. 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The most notable challenge is the coordination of multi-domain and multi-timescale system dynamics. This thesis seeks to address the challenge of coordination between the slow battery state of charge dynamic and faster electro-mechanical dynamics for a hybrid unmanned aerial vehicle. The graph-based modeling framework for multi-domain systems is leveraged to capture the interactions between relevant energy domains. Additionally, the modularity and scalability of this modeling approach is used to develop a dynamic model for a hybrid unmanned aerial vehicle. The system model facilities the design and development of three control architectures of varying complexity. A baseline controller is developed for sake of comparison. A battery state of charge bounding algorithm in integrated into a centralized model predictive controller to provide system coordination across timescales. Lastly, an alternative model predictive hierarchical controller is designed to provide real-time planning of the slow battery state of charge dynamics. The proposed models and controllers are experimentally validated on a novel hybrid electric UAV powertrain testbed. The controllers are evaluated on three core figures of merit: performance, reliability, and efficiency. Both simulation and experimental results show that the advanced controllers outperform the baseline in all figures of merit with a 9-12.5% reduction in fuel usage.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Christopher Aksland, accepted the attached license on 2019-12-11 at 10:51.","The student, Christopher Aksland, submitted this Thesis for approval on 2019-12-11 at 11:00.","This Thesis was approved for publication on 2019-12-12 at 11:20.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14786 on 2020-02-28 at 17:16:45","Made available in DSpace on 2020-03-02T22:03:25Z (GMT). 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