{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1504"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1504","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Design optimization of articulated vehicles with autonomous steering control","abstract":"Human driver errors cause about 94% of traffic collisions. To increase the safety of road vehicles, extensive studies have been conducted on developing autonomous driving technologies. However, little attention has been paid to exploring autonomous articulated vehicles (AVs). This thesis proposes an approach to the design synthesis of AVs with autonomous steering. A linear yaw-plane model is generated to represent the AV, and a model predictive control (MPC) based tracking controller is designed for steering control. A stochastic modeling technique is developed using Monte-Carlo method to evaluate the performance limitations of AV dynamics. For enhancing the performance of the selfsteering AV, the design synthesis is formulated as a bi-layer design optimization problem. Particle Swarm Optimization (PSO) and Differential Evolution (DE) are introduced and tested. Selected simulation results are presented and discussed, and the insightful findings may be used as guidelines for developing autonomous driving control systems of AVs.","abstract_html":"Human driver errors cause about 94% of traffic collisions. To increase the safety of road vehicles, extensive studies have been conducted on developing autonomous driving technologies. However, little attention has been paid to exploring autonomous articulated vehicles (AVs). This thesis proposes an approach to the design synthesis of AVs with autonomous steering. A linear yaw-plane model is generated to represent the AV, and a model predictive control (MPC) based tracking controller is designed for steering control. A stochastic modeling technique is developed using Monte-Carlo method to evaluate the performance limitations of AV dynamics. For enhancing the performance of the selfsteering AV, the design synthesis is formulated as a bi-layer design optimization problem. Particle Swarm Optimization (PSO) and Differential Evolution (DE) are introduced and tested. Selected simulation results are presented and discussed, and the insightful findings may be used as guidelines for developing autonomous driving control systems of AVs.","abstract_has_math":false,"creators":["Yu, Jiangtao"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["He, Yuping"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08-01","date_published":"2022-08-01","updated_at":"2026-07-24T05:35:39Z","subjects":["Autonomous articulated vehicles","MPC based tracking controller","Design synthesis approach","Numerical simulation","Lateral vehicle dynamics"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1504","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["He, Yuping"]},{"key":"dc:creator","label":"Author","values":["Yu, Jiangtao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-08-29T20:39:06Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-08-29T20:39:06Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-08-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical 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 articulated vehicles","MPC based tracking controller","Design synthesis approach","Numerical simulation","Lateral vehicle dynamics"]}]},{"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/10155/1504"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Human driver errors cause about 94% of traffic collisions. To increase the safety of road vehicles, extensive studies have been conducted on developing autonomous driving technologies. However, little attention has been paid to exploring autonomous articulated vehicles (AVs). This thesis proposes an approach to the design synthesis of AVs with autonomous steering. A linear yaw-plane model is generated to represent the AV, and a model predictive control (MPC) based tracking controller is designed for steering control. A stochastic modeling technique is developed using Monte-Carlo method to evaluate the performance limitations of AV dynamics. For enhancing the performance of the selfsteering AV, the design synthesis is formulated as a bi-layer design optimization problem. Particle Swarm Optimization (PSO) and Differential Evolution (DE) are introduced and tested. Selected simulation results are presented and discussed, and the insightful findings may be used as guidelines for developing autonomous driving control systems of AVs."]},{"key":"dc:title","label":"Title","values":["Design optimization of articulated vehicles with autonomous steering control"]}]}],"canonical_facts":{"dc:contributor.advisor":["He, Yuping"],"dc:creator":["Yu, Jiangtao"],"dc:date.accessioned":["2022-08-29T20:39:06Z"],"dc:date.available":["2022-08-29T20:39:06Z"],"dc:date.issued":["2022-08-01"],"dc:description.abstract":["Human driver errors cause about 94% of traffic collisions. To increase the safety of road vehicles, extensive studies have been conducted on developing autonomous driving technologies. However, little attention has been paid to exploring autonomous articulated vehicles (AVs). This thesis proposes an approach to the design synthesis of AVs with autonomous steering. A linear yaw-plane model is generated to represent the AV, and a model predictive control (MPC) based tracking controller is designed for steering control. A stochastic modeling technique is developed using Monte-Carlo method to evaluate the performance limitations of AV dynamics. For enhancing the performance of the selfsteering AV, the design synthesis is formulated as a bi-layer design optimization problem. Particle Swarm Optimization (PSO) and Differential Evolution (DE) are introduced and tested. Selected simulation results are presented and discussed, and the insightful findings may be used as guidelines for developing autonomous driving control systems of AVs."],"dc:identifier.uri":["https://hdl.handle.net/10155/1504"],"dc:language.iso":["en"],"dc:subject":["Autonomous articulated vehicles","MPC based tracking controller","Design synthesis approach","Numerical simulation","Lateral vehicle dynamics"],"dc:title":["Design optimization of articulated vehicles with autonomous steering control"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:39Z"}