{"id":{"repo_id":"calpoly","oai_identifier":"oai:digitalcommons.calpoly.edu:theses-1019"},"canonical_url":"https://search.dev.ndltd.org/etd/calpoly/oai:digitalcommons.calpoly.edu:theses-1019","repository":{"repo_id":"calpoly","name":"Cal Poly","base_url":"https://digitalcommons.calpoly.edu/do/oai/"},"display":{"title":"Automotive Suspension Parameter Estimation Using Smart Wireless Sensor Technology","abstract":"This thesis project demonstrates the feasibility of using a smart sensor system to estimate vehicle parameters. It includes the development of the smart sensor system and the method for which vehicle parameters are estimated using this system. The smart sensor system is a wireless computer controlled sensor array that can be easily installed onto a vehicle. Parameter estimation was accomplished using grey box code in Matlab System Identification Toolbox, a software package from Mathworks. Front and rear suspension damping rates and pitch inertia were estimated on the 2008 Cal Poly SAE Baja Car with good accuracy during testing.","abstract_html":"This thesis project demonstrates the feasibility of using a smart sensor system to estimate vehicle parameters. It includes the development of the smart sensor system and the method for which vehicle parameters are estimated using this system. The smart sensor system is a wireless computer controlled sensor array that can be easily installed onto a vehicle. Parameter estimation was accomplished using grey box code in Matlab System Identification Toolbox, a software package from Mathworks. Front and rear suspension damping rates and pitch inertia were estimated on the 2008 Cal Poly SAE Baja Car with good accuracy during testing.","abstract_has_math":false,"creators":["Hoffman, Samuel Chase"],"institution":null,"degree_name":"MS in Mechanical Engineering","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["John Ridgely"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008-05-01T07:00:00Z","date_published":"2008-05-01T07:00:00Z","updated_at":"2026-07-24T01:30:51Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2008.8"],"render_values":[{"text":"10.15368/theses.2008.8","href":"https://doi.org/10.15368/theses.2008.8","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/20","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["John Ridgely"]},{"key":"dc:creator","label":"Author","values":["Hoffman, Samuel Chase"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-10-22T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Mechanical Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/20","10.15368/theses.2008.8"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis project demonstrates the feasibility of using a smart sensor system to estimate vehicle parameters. It includes the development of the smart sensor system and the method for which vehicle parameters are estimated using this system. The smart sensor system is a wireless computer controlled sensor array that can be easily installed onto a vehicle. Parameter estimation was accomplished using grey box code in Matlab System Identification Toolbox, a software package from Mathworks. Front and rear suspension damping rates and pitch inertia were estimated on the 2008 Cal Poly SAE Baja Car with good accuracy during testing."]},{"key":"dc:title","label":"Title","values":["Automotive Suspension Parameter Estimation Using Smart Wireless Sensor Technology"]}]}],"canonical_facts":{"dc:contributor":["John Ridgely"],"dc:creator":["Hoffman, Samuel Chase"],"dc:date.available":["2019-10-22T07:00:00Z"],"dc:description.abstract":["This thesis project demonstrates the feasibility of using a smart sensor system to estimate vehicle parameters. It includes the development of the smart sensor system and the method for which vehicle parameters are estimated using this system. The smart sensor system is a wireless computer controlled sensor array that can be easily installed onto a vehicle. Parameter estimation was accomplished using grey box code in Matlab System Identification Toolbox, a software package from Mathworks. Front and rear suspension damping rates and pitch inertia were estimated on the 2008 Cal Poly SAE Baja Car with good accuracy during testing."],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/20","10.15368/theses.2008.8"],"dc:title":["Automotive Suspension Parameter Estimation Using Smart Wireless Sensor Technology"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_name":["MS in Mechanical Engineering"]},"updated_at":"2026-07-24T01:30:51Z"}