Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begins with an overview of traditional SYSID methods and then introduces PIRL as a modification of standard RL. The research applies PIRL to short-period aircraft dynamics, demonstrating its effectiveness in both offline and online learning frameworks. The dissertation then further explores PIRL’s utility in an indirect model reference adaptive control (IMRAC) implementation. Finally, PIRL is applied to an autonomous underwater vehicle (AUV) and paired with a control algorithm known as Adaptive Nested Nonlinear Dynamic Inversion Control (ANNDI). Results indicate that PIRL can more accurately identify aerodynamic coefficients and the system matrix values for short-period aircraft dynamics, when compared to conventional RL. Additionally, PIRL has been shown to be effective at estimating the mass of an AUV, both in simulation studies and on-board a real AUV, providing accurate dynamics models to an adaptive controller, increasing the vehicle’s controllability in mass-changing environments.
","abstract_html":"<p>Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begins with an overview of traditional SYSID methods and then introduces PIRL as a modification of standard RL. The research applies PIRL to short-period aircraft dynamics, demonstrating its effectiveness in both offline and online learning frameworks. The dissertation then further explores PIRL’s utility in an indirect model reference adaptive control (IMRAC) implementation. Finally, PIRL is applied to an autonomous underwater vehicle (AUV) and paired with a control algorithm known as Adaptive Nested Nonlinear Dynamic Inversion Control (ANNDI). Results indicate that PIRL can more accurately identify aerodynamic coefficients and the system matrix values for short-period aircraft dynamics, when compared to conventional RL. Additionally, PIRL has been shown to be effective at estimating the mass of an AUV, both in simulation studies and on-board a real AUV, providing accurate dynamics models to an adaptive controller, increasing the vehicle’s controllability in mass-changing environments.</p>","abstract_has_math":false,"creators":["Schaff, Nathan"],"institution":null,"degree_name":"Doctor of Philosophy in Aerospace Engineering","degree_level":"Dissertation - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-11T08:00:00Z","date_published":"2025-12-11T08:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["AI","PIRL","System Identification","Artificial Intelligence","Parameter Informed Reinforcement Learning","Aircraft","AUV","Artificial Intelligence and Robotics","Controls and Control Theory","Data Science","Navigation, Guidance, Control and Dynamics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/999","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Schaff, Nathan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["AI","PIRL","System Identification","Artificial Intelligence","Parameter Informed Reinforcement Learning","Aircraft","AUV","Artificial Intelligence and Robotics","Controls and Control Theory","Data Science","Navigation, Guidance, Control and Dynamics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/999"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begins with an overview of traditional SYSID methods and then introduces PIRL as a modification of standard RL. The research applies PIRL to short-period aircraft dynamics, demonstrating its effectiveness in both offline and online learning frameworks. The dissertation then further explores PIRL’s utility in an indirect model reference adaptive control (IMRAC) implementation. Finally, PIRL is applied to an autonomous underwater vehicle (AUV) and paired with a control algorithm known as Adaptive Nested Nonlinear Dynamic Inversion Control (ANNDI). Results indicate that PIRL can more accurately identify aerodynamic coefficients and the system matrix values for short-period aircraft dynamics, when compared to conventional RL. Additionally, PIRL has been shown to be effective at estimating the mass of an AUV, both in simulation studies and on-board a real AUV, providing accurate dynamics models to an adaptive controller, increasing the vehicle’s controllability in mass-changing environments.
"]},{"key":"dc:title","label":"Title","values":["Parameter Informed Reinforcement Learning for Vehicle System Identification"]}]}],"canonical_facts":{"dc:creator":["Schaff, Nathan"],"dc:description.abstract":["Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begins with an overview of traditional SYSID methods and then introduces PIRL as a modification of standard RL. The research applies PIRL to short-period aircraft dynamics, demonstrating its effectiveness in both offline and online learning frameworks. The dissertation then further explores PIRL’s utility in an indirect model reference adaptive control (IMRAC) implementation. Finally, PIRL is applied to an autonomous underwater vehicle (AUV) and paired with a control algorithm known as Adaptive Nested Nonlinear Dynamic Inversion Control (ANNDI). Results indicate that PIRL can more accurately identify aerodynamic coefficients and the system matrix values for short-period aircraft dynamics, when compared to conventional RL. Additionally, PIRL has been shown to be effective at estimating the mass of an AUV, both in simulation studies and on-board a real AUV, providing accurate dynamics models to an adaptive controller, increasing the vehicle’s controllability in mass-changing environments.
"],"dc:identifier":["https://commons.erau.edu/edt/999"],"dc:subject":["AI","PIRL","System Identification","Artificial Intelligence","Parameter Informed Reinforcement Learning","Aircraft","AUV","Artificial Intelligence and Robotics","Controls and Control Theory","Data Science","Navigation, Guidance, Control and Dynamics"],"dc:title":["Parameter Informed Reinforcement Learning for Vehicle System Identification"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Aerospace Engineering"]},"updated_at":"2026-07-27T19:26:22Z"}