{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99285"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99285","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A framework for the control of electro-thermal aircraft power systems","abstract":"This dissertation presents a hierarchical controller framework that utilizes model predictive controllers at multiple time scales in order to manage the operation of aircraft power systems. With current and next generation aircraft replacing traditional power systems with electrically powered components, the coupling between an aircraft engine, electrical system, and thermal management system is becoming increasingly more complex. This presents a unique control problem that requires coordination between the generation, distribution, and consumption of power on board an aircraft, while also maintaining performance guarantees for various systems. The proposed hierarchical control framework splits decision making into multiple levels with each level having a unique update rate. At upper levels, controllers are designed with prediction horizons that can estimate plant performance one hour into the future. Using this extended prediction horizon, the upper level controllers generate references to pass down the hierarchy to lower level controllers. At the lower levels, controllers focus on tracking references from high level controllers while also mitigating high-frequency disturbances. The combination of slow update, long prediction horizon controllers with fast update, short prediction horizon controllers enables the hierarchical control framework to achieve excellent performance and disturbance rejection. A candidate aircraft power system is developed in MATLAB/Simulink using high-fidelity component models. Graph-based modeling techniques are used to generate suitable models for MPC controllers at each layer of the hierarchical control structure. The proposed hierarchical control framework is tested on the high-fidelity Simulink model and compared to a baseline logic and PI controller. Controllers are evaluated on figures of merit including specific fuel consumption, thermal endurance, and remaining thermal capacitance at the end of a mission. Results show that the proposed control approach is capable of making thermally-conscious electrical system decisions to help reduce the amount of waste heat generated by the aircraft in order to achieve mission success.","abstract_html":"This dissertation presents a hierarchical controller framework that utilizes model predictive controllers at multiple time scales in order to manage the operation of aircraft power systems. With current and next generation aircraft replacing traditional power systems with electrically powered components, the coupling between an aircraft engine, electrical system, and thermal management system is becoming increasingly more complex. This presents a unique control problem that requires coordination between the generation, distribution, and consumption of power on board an aircraft, while also maintaining performance guarantees for various systems. The proposed hierarchical control framework splits decision making into multiple levels with each level having a unique update rate. At upper levels, controllers are designed with prediction horizons that can estimate plant performance one hour into the future. Using this extended prediction horizon, the upper level controllers generate references to pass down the hierarchy to lower level controllers. At the lower levels, controllers focus on tracking references from high level controllers while also mitigating high-frequency disturbances. The combination of slow update, long prediction horizon controllers with fast update, short prediction horizon controllers enables the hierarchical control framework to achieve excellent performance and disturbance rejection. A candidate aircraft power system is developed in MATLAB/Simulink using high-fidelity component models. Graph-based modeling techniques are used to generate suitable models for MPC controllers at each layer of the hierarchical control structure. The proposed hierarchical control framework is tested on the high-fidelity Simulink model and compared to a baseline logic and PI controller. Controllers are evaluated on figures of merit including specific fuel consumption, thermal endurance, and remaining thermal capacitance at the end of a mission. Results show that the proposed control approach is capable of making thermally-conscious electrical system decisions to help reduce the amount of waste heat generated by the aircraft in order to achieve mission success.","abstract_has_math":false,"creators":["Williams, Matthew A."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Alleyne, Andrew","Mehta, Prashant","Pilawa, Robert","Hencey, Brandon"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:44:42Z","date_published":"2018-03-13T15:44:42Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Hierarchical control","Model predictive control","Aircraft power systems","Energy management","Graph-based modeling","Control systems"],"languages":["en"],"rights":["Copyright 2017 Matthew A. Williams"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99285","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alleyne, Andrew","Mehta, Prashant","Pilawa, Robert","Hencey, Brandon"]},{"key":"dc:creator","label":"Author","values":["Williams, Matthew A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:44:42Z","2017-09-07","2017-12"]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Hierarchical control","Model predictive control","Aircraft power systems","Energy management","Graph-based modeling","Control systems"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Matthew A. Williams"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99285"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This dissertation presents a hierarchical controller framework that utilizes model predictive controllers at multiple time scales in order to manage the operation of aircraft power systems. With current and next generation aircraft replacing traditional power systems with electrically powered components, the coupling between an aircraft engine, electrical system, and thermal management system is becoming increasingly more complex. This presents a unique control problem that requires coordination between the generation, distribution, and consumption of power on board an aircraft, while also maintaining performance guarantees for various systems. The proposed hierarchical control framework splits decision making into multiple levels with each level having a unique update rate. At upper levels, controllers are designed with prediction horizons that can estimate plant performance one hour into the future. Using this extended prediction horizon, the upper level controllers generate references to pass down the hierarchy to lower level controllers. At the lower levels, controllers focus on tracking references from high level controllers while also mitigating high-frequency disturbances. The combination of slow update, long prediction horizon controllers with fast update, short prediction horizon controllers enables the hierarchical control framework to achieve excellent performance and disturbance rejection. A candidate aircraft power system is developed in MATLAB/Simulink using high-fidelity component models. Graph-based modeling techniques are used to generate suitable models for MPC controllers at each layer of the hierarchical control structure. The proposed hierarchical control framework is tested on the high-fidelity Simulink model and compared to a baseline logic and PI controller. Controllers are evaluated on figures of merit including specific fuel consumption, thermal endurance, and remaining thermal capacitance at the end of a mission. Results show that the proposed control approach is capable of making thermally-conscious electrical system decisions to help reduce the amount of waste heat generated by the aircraft in order to achieve mission success.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Matthew Williams, accepted the attached license on 2017-09-01 at 09:47.","The student, Matthew Williams, submitted this Dissertation for approval on 2017-09-01 at 10:05.","This Dissertation was approved for publication on 2017-09-07 at 13:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11631 on 2018-03-13 at 10:03:23","Made available in DSpace on 2018-03-13T15:44:42Z (GMT). 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With current and next generation aircraft replacing traditional power systems with electrically powered components, the coupling between an aircraft engine, electrical system, and thermal management system is becoming increasingly more complex. This presents a unique control problem that requires coordination between the generation, distribution, and consumption of power on board an aircraft, while also maintaining performance guarantees for various systems. The proposed hierarchical control framework splits decision making into multiple levels with each level having a unique update rate. At upper levels, controllers are designed with prediction horizons that can estimate plant performance one hour into the future. Using this extended prediction horizon, the upper level controllers generate references to pass down the hierarchy to lower level controllers. At the lower levels, controllers focus on tracking references from high level controllers while also mitigating high-frequency disturbances. The combination of slow update, long prediction horizon controllers with fast update, short prediction horizon controllers enables the hierarchical control framework to achieve excellent performance and disturbance rejection. A candidate aircraft power system is developed in MATLAB/Simulink using high-fidelity component models. Graph-based modeling techniques are used to generate suitable models for MPC controllers at each layer of the hierarchical control structure. The proposed hierarchical control framework is tested on the high-fidelity Simulink model and compared to a baseline logic and PI controller. Controllers are evaluated on figures of merit including specific fuel consumption, thermal endurance, and remaining thermal capacitance at the end of a mission. Results show that the proposed control approach is capable of making thermally-conscious electrical system decisions to help reduce the amount of waste heat generated by the aircraft in order to achieve mission success.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Matthew Williams, accepted the attached license on 2017-09-01 at 09:47.","The student, Matthew Williams, submitted this Dissertation for approval on 2017-09-01 at 10:05.","This Dissertation was approved for publication on 2017-09-07 at 13:40.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11631 on 2018-03-13 at 10:03:23","Made available in DSpace on 2018-03-13T15:44:42Z (GMT). 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