{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1584"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1584","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Design synthesis of NLMPC-based tracking controller for autonomous vehicles with active aerodynamic control","abstract":"The past three decades have witnessed extensive studies on tracking-control for autonomous vehicles (AVs). However, there is a lack of studies on effective design methods in this field. To tackle this problem, this thesis proposes a design synthesis method which is featured a design framework with two layers: at the upper layer, a particle swarm optimization algorithm is used to find optimal solutions with desired trajectory-tracking performance; at the lower layer, a comprehensively coupled dynamic analysis is conducted among the three subsystems, including a nonlinear vehicle model with active aerodynamic control for mechanical vehicle representation, a motion-planning module with given perception data, and a tracking controller based on non-linear model predictive control (NLMPC) for direction and lateral stability control. The design optimization demonstrates that the proposed method can effectively determine the desired design variables to achieve optimal trajectory-tracking performance. The insightful findings from this study will provide valuable guidelines for designing autonomous vehicles.","abstract_html":"The past three decades have witnessed extensive studies on tracking-control for autonomous vehicles (AVs). However, there is a lack of studies on effective design methods in this field. To tackle this problem, this thesis proposes a design synthesis method which is featured a design framework with two layers: at the upper layer, a particle swarm optimization algorithm is used to find optimal solutions with desired trajectory-tracking performance; at the lower layer, a comprehensively coupled dynamic analysis is conducted among the three subsystems, including a nonlinear vehicle model with active aerodynamic control for mechanical vehicle representation, a motion-planning module with given perception data, and a tracking controller based on non-linear model predictive control (NLMPC) for direction and lateral stability control. The design optimization demonstrates that the proposed method can effectively determine the desired design variables to achieve optimal trajectory-tracking performance. The insightful findings from this study will provide valuable guidelines for designing autonomous vehicles.","abstract_has_math":false,"creators":["Mao, Chunyu"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Automotive Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["He, Yuping","Agelin-Chaab, Martin"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12-01","date_published":"2022-12-01","updated_at":"2026-07-24T05:35:38Z","subjects":["Autonomous vehicles","Autonomous steering control","Trajectory-tracking","Non-linear model predictive control","Active aerodynamic control"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1584","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["He, Yuping","Agelin-Chaab, Martin"]},{"key":"dc:creator","label":"Author","values":["Mao, Chunyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-03-13T16:24:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-13T16:24:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Automotive Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Autonomous vehicles","Autonomous steering control","Trajectory-tracking","Non-linear model predictive control","Active aerodynamic control"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1584"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The past three decades have witnessed extensive studies on tracking-control for autonomous vehicles (AVs). However, there is a lack of studies on effective design methods in this field. To tackle this problem, this thesis proposes a design synthesis method which is featured a design framework with two layers: at the upper layer, a particle swarm optimization algorithm is used to find optimal solutions with desired trajectory-tracking performance; at the lower layer, a comprehensively coupled dynamic analysis is conducted among the three subsystems, including a nonlinear vehicle model with active aerodynamic control for mechanical vehicle representation, a motion-planning module with given perception data, and a tracking controller based on non-linear model predictive control (NLMPC) for direction and lateral stability control. The design optimization demonstrates that the proposed method can effectively determine the desired design variables to achieve optimal trajectory-tracking performance. 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To tackle this problem, this thesis proposes a design synthesis method which is featured a design framework with two layers: at the upper layer, a particle swarm optimization algorithm is used to find optimal solutions with desired trajectory-tracking performance; at the lower layer, a comprehensively coupled dynamic analysis is conducted among the three subsystems, including a nonlinear vehicle model with active aerodynamic control for mechanical vehicle representation, a motion-planning module with given perception data, and a tracking controller based on non-linear model predictive control (NLMPC) for direction and lateral stability control. The design optimization demonstrates that the proposed method can effectively determine the desired design variables to achieve optimal trajectory-tracking performance. 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