{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1559"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1559","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Lateral stability analysis and MPC tracking control for articulated heavy vehicles","abstract":"Articulated heavy vehicles (AHVs) exhibit poor maneuverability during curved-path negotiations and low lateral stability under high-speed evasive maneuvers, which may lead to unstable motion modes, e.g., trailer-sway and jackknifing, causing severe accidents. However, vehicle parameters that can improve the static stability may degrade the dynamic stability. Therefore, to design controllers for improving the stability of AHVs, the trade-off between the static and dynamic instabilities is a necessary research topic. To analyze this trade-off, three different trailer payload schemes and two different tractor rear axle arrangements are considered. This trade-off is quantified using numerical simulations. Building upon the above trade-off analysis, this study designs an active safety technique in terms of a tracking-controller based on nonlinear model predictive control (NLMPC) for autonomous AHVs. With the proposed tracking-controller, the AHV tracks the predefined reference path and follows a planned forward speed scheme. Numerical simulation demonstrates the effectiveness of the proposed NLMPC tracking-controller.","abstract_html":"Articulated heavy vehicles (AHVs) exhibit poor maneuverability during curved-path negotiations and low lateral stability under high-speed evasive maneuvers, which may lead to unstable motion modes, e.g., trailer-sway and jackknifing, causing severe accidents. However, vehicle parameters that can improve the static stability may degrade the dynamic stability. Therefore, to design controllers for improving the stability of AHVs, the trade-off between the static and dynamic instabilities is a necessary research topic. To analyze this trade-off, three different trailer payload schemes and two different tractor rear axle arrangements are considered. This trade-off is quantified using numerical simulations. Building upon the above trade-off analysis, this study designs an active safety technique in terms of a tracking-controller based on nonlinear model predictive control (NLMPC) for autonomous AHVs. With the proposed tracking-controller, the AHV tracks the predefined reference path and follows a planned forward speed scheme. Numerical simulation demonstrates the effectiveness of the proposed NLMPC tracking-controller.","abstract_has_math":false,"creators":["Sharma, Tarun"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["He, Yuping"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-10-01","date_published":"2022-10-01","updated_at":"2026-07-24T05:35:16Z","subjects":["Articulated heavy vehicles","Static stability","Dynamic stability","Trade-off analysis","Autonomous vehicles"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1559","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":["Sharma, Tarun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-11-25T17:37:46Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-11-25T17:37:46Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-10-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["Articulated heavy vehicles","Static stability","Dynamic stability","Trade-off analysis","Autonomous vehicles"]}]},{"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/10155/1559"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Articulated heavy vehicles (AHVs) exhibit poor maneuverability during curved-path negotiations and low lateral stability under high-speed evasive maneuvers, which may lead to unstable motion modes, e.g., trailer-sway and jackknifing, causing severe accidents. However, vehicle parameters that can improve the static stability may degrade the dynamic stability. Therefore, to design controllers for improving the stability of AHVs, the trade-off between the static and dynamic instabilities is a necessary research topic. To analyze this trade-off, three different trailer payload schemes and two different tractor rear axle arrangements are considered. This trade-off is quantified using numerical simulations. Building upon the above trade-off analysis, this study designs an active safety technique in terms of a tracking-controller based on nonlinear model predictive control (NLMPC) for autonomous AHVs. With the proposed tracking-controller, the AHV tracks the predefined reference path and follows a planned forward speed scheme. Numerical simulation demonstrates the effectiveness of the proposed NLMPC tracking-controller."]},{"key":"dc:title","label":"Title","values":["Lateral stability analysis and MPC tracking control for articulated heavy vehicles"]}]}],"canonical_facts":{"dc:contributor.advisor":["He, Yuping"],"dc:creator":["Sharma, Tarun"],"dc:date.accessioned":["2022-11-25T17:37:46Z"],"dc:date.available":["2022-11-25T17:37:46Z"],"dc:date.issued":["2022-10-01"],"dc:description.abstract":["Articulated heavy vehicles (AHVs) exhibit poor maneuverability during curved-path negotiations and low lateral stability under high-speed evasive maneuvers, which may lead to unstable motion modes, e.g., trailer-sway and jackknifing, causing severe accidents. However, vehicle parameters that can improve the static stability may degrade the dynamic stability. Therefore, to design controllers for improving the stability of AHVs, the trade-off between the static and dynamic instabilities is a necessary research topic. To analyze this trade-off, three different trailer payload schemes and two different tractor rear axle arrangements are considered. This trade-off is quantified using numerical simulations. Building upon the above trade-off analysis, this study designs an active safety technique in terms of a tracking-controller based on nonlinear model predictive control (NLMPC) for autonomous AHVs. With the proposed tracking-controller, the AHV tracks the predefined reference path and follows a planned forward speed scheme. Numerical simulation demonstrates the effectiveness of the proposed NLMPC tracking-controller."],"dc:identifier.uri":["https://hdl.handle.net/10155/1559"],"dc:language.iso":["en"],"dc:subject":["Articulated heavy vehicles","Static stability","Dynamic stability","Trade-off analysis","Autonomous vehicles"],"dc:title":["Lateral stability analysis and MPC tracking control for articulated heavy vehicles"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Applied Science (MASc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:16Z"}