{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/68835"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/68835","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"In-Flight Learning Based Flight Control of an Unmanned Aircraft System","abstract":"Unmanned Aerial Vehicles (UAVs) popularity has increased substantially in the last few years. UAVs capabilities continue to improve as a result of advances in battery technology, communication, navigation systems and electronics. Increased popularity has driven researchers to improve UAVs reliability and safety which is reflected by the number of publications and accelerating educational programs interest. UAVs are suited for a wide range of civilian and military applications; however, UAVs currently can not integrate with civilian airspace because of stringent safety requirements. Hence, it is necessary to push the envelope for UAVs design and control so that they can learn from nature and have more self-aware capabilities to improve safety and reliability. This dissertation addresses some challenges involved with flight controller learning based on real-time modeling of UAV. Plenty of UAV applications require different operational capabilities within a composite mission. These capabilities include landing and taking off using short runways, while being able to perform missions that require a high cruise speed i.e. tracking applications. A composite mission also requires the aircraft to be able to hover or operate with low cruise speeds for applications involving stationary moments. All of these different operational modes require a hybrid aircraft design that combines fixed wing aircraft capabilities and Vertical Take-Off and Landing (VTOL) aircraft capabilities. However, extensive resources required for hybrid aircraft design prohibited the discovery of different revolutionary designs. The work presented in this dissertation describes the development of a rapid modeling, prototyping and controller design platform of an unmanned quadrotor aircraft. Three main objectives are investigated: intelligent excitation input design, real-time parameter estimation, and learning control. Real-time estimation of dynamic model parameters is important for control adaptation. However, the aircraft model estimation performance can be severely degraded by an active control system and highly collinear model terms such as those found on a quadrotor unmanned aircraft. Recursive Fourier Transform Regression was applied to estimate parameters of different model forms/structures and using different excitation levels. The generated models are utilized to reconfigure a Nonlinear Dynamic Inversion (NDI) controller considering different testing conditions: normal, failure, and learning flights. Finally,an intelligent input design technique is proposed which enables autonomous identification of the vehicle’s response modal frequencies and emphasizes excitation power accordingly.","abstract_html":"Unmanned Aerial Vehicles (UAVs) popularity has increased substantially in the last few years. UAVs capabilities continue to improve as a result of advances in battery technology, communication, navigation systems and electronics. Increased popularity has driven researchers to improve UAVs reliability and safety which is reflected by the number of publications and accelerating educational programs interest. UAVs are suited for a wide range of civilian and military applications; however, UAVs currently can not integrate with civilian airspace because of stringent safety requirements. Hence, it is necessary to push the envelope for UAVs design and control so that they can learn from nature and have more self-aware capabilities to improve safety and reliability. This dissertation addresses some challenges involved with flight controller learning based on real-time modeling of UAV. Plenty of UAV applications require different operational capabilities within a composite mission. These capabilities include landing and taking off using short runways, while being able to perform missions that require a high cruise speed i.e. tracking applications. A composite mission also requires the aircraft to be able to hover or operate with low cruise speeds for applications involving stationary moments. All of these different operational modes require a hybrid aircraft design that combines fixed wing aircraft capabilities and Vertical Take-Off and Landing (VTOL) aircraft capabilities. However, extensive resources required for hybrid aircraft design prohibited the discovery of different revolutionary designs. The work presented in this dissertation describes the development of a rapid modeling, prototyping and controller design platform of an unmanned quadrotor aircraft. Three main objectives are investigated: intelligent excitation input design, real-time parameter estimation, and learning control. Real-time estimation of dynamic model parameters is important for control adaptation. However, the aircraft model estimation performance can be severely degraded by an active control system and highly collinear model terms such as those found on a quadrotor unmanned aircraft. Recursive Fourier Transform Regression was applied to estimate parameters of different model forms/structures and using different excitation levels. The generated models are utilized to reconfigure a Nonlinear Dynamic Inversion (NDI) controller considering different testing conditions: normal, failure, and learning flights. Finally,an intelligent input design technique is proposed which enables autonomous identification of the vehicle’s response modal frequencies and emphasizes excitation power accordingly.","abstract_has_math":false,"creators":["Alabsi, Mohammed"],"institution":"University of Missouri -- Kansas City","degree_name":"Ph.D. (Doctor of Philosophy)","degree_level":"Doctoral","degree_discipline":"Engineering (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Fields, Travis"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-24T05:17:53Z","subjects":[],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/68835","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Fields, Travis"]},{"key":"dc:creator","label":"Author","values":["Alabsi, Mohammed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-05-31T19:47:23Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-05-31T19:47:23Z"]},{"key":"dc:date.issued","label":"Date","values":["2018"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri -- Kansas City"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering (UMKC)","Electrical and Computer Engineering (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D. (Doctor of Philosophy)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/68835"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page viewed June 3, 2019","Dissertation advisor: Travis Fields","Vita","Includes bibliographical references (pages 128-137)","Thesis (PH.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2018"]},{"key":"dc:description.abstract","label":"Abstract","values":["Unmanned Aerial Vehicles (UAVs) popularity has increased substantially in the last few years. UAVs capabilities continue to improve as a result of advances in battery technology, communication, navigation systems and electronics. Increased popularity has driven researchers to improve UAVs reliability and safety which is reflected by the number of publications and accelerating educational programs interest. UAVs are suited for a wide range of civilian and military applications; however, UAVs currently can not integrate with civilian airspace because of stringent safety requirements. Hence, it is necessary to push the envelope for UAVs design and control so that they can learn from nature and have more self-aware capabilities to improve safety and reliability. This dissertation addresses some challenges involved with flight controller learning based on real-time modeling of UAV. Plenty of UAV applications require different operational capabilities within a composite mission. These capabilities include landing and taking off using short runways, while being able to perform missions that require a high cruise speed i.e. tracking applications. A composite mission also requires the aircraft to be able to hover or operate with low cruise speeds for applications involving stationary moments. All of these different operational modes require a hybrid aircraft design that combines fixed wing aircraft capabilities and Vertical Take-Off and Landing (VTOL) aircraft capabilities. However, extensive resources required for hybrid aircraft design prohibited the discovery of different revolutionary designs. The work presented in this dissertation describes the development of a rapid modeling, prototyping and controller design platform of an unmanned quadrotor aircraft. Three main objectives are investigated: intelligent excitation input design, real-time parameter estimation, and learning control. Real-time estimation of dynamic model parameters is important for control adaptation. However, the aircraft model estimation performance can be severely degraded by an active control system and highly collinear model terms such as those found on a quadrotor unmanned aircraft. Recursive Fourier Transform Regression was applied to estimate parameters of different model forms/structures and using different excitation levels. The generated models are utilized to reconfigure a Nonlinear Dynamic Inversion (NDI) controller considering different testing conditions: normal, failure, and learning flights. Finally,an intelligent input design technique is proposed which enables autonomous identification of the vehicle’s response modal frequencies and emphasizes excitation power accordingly."]},{"key":"dc:title","label":"Title","values":["In-Flight Learning Based Flight Control of an Unmanned Aircraft System"]}]}],"canonical_facts":{"dc:contributor.advisor":["Fields, Travis"],"dc:creator":["Alabsi, Mohammed"],"dc:date.accessioned":["2019-05-31T19:47:23Z"],"dc:date.available":["2019-05-31T19:47:23Z"],"dc:date.issued":["2018"],"dc:description":["Title from PDF of title page viewed June 3, 2019","Dissertation advisor: Travis Fields","Vita","Includes bibliographical references (pages 128-137)","Thesis (PH.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2018"],"dc:description.abstract":["Unmanned Aerial Vehicles (UAVs) popularity has increased substantially in the last few years. UAVs capabilities continue to improve as a result of advances in battery technology, communication, navigation systems and electronics. Increased popularity has driven researchers to improve UAVs reliability and safety which is reflected by the number of publications and accelerating educational programs interest. UAVs are suited for a wide range of civilian and military applications; however, UAVs currently can not integrate with civilian airspace because of stringent safety requirements. Hence, it is necessary to push the envelope for UAVs design and control so that they can learn from nature and have more self-aware capabilities to improve safety and reliability. This dissertation addresses some challenges involved with flight controller learning based on real-time modeling of UAV. Plenty of UAV applications require different operational capabilities within a composite mission. These capabilities include landing and taking off using short runways, while being able to perform missions that require a high cruise speed i.e. tracking applications. A composite mission also requires the aircraft to be able to hover or operate with low cruise speeds for applications involving stationary moments. All of these different operational modes require a hybrid aircraft design that combines fixed wing aircraft capabilities and Vertical Take-Off and Landing (VTOL) aircraft capabilities. However, extensive resources required for hybrid aircraft design prohibited the discovery of different revolutionary designs. The work presented in this dissertation describes the development of a rapid modeling, prototyping and controller design platform of an unmanned quadrotor aircraft. Three main objectives are investigated: intelligent excitation input design, real-time parameter estimation, and learning control. Real-time estimation of dynamic model parameters is important for control adaptation. However, the aircraft model estimation performance can be severely degraded by an active control system and highly collinear model terms such as those found on a quadrotor unmanned aircraft. Recursive Fourier Transform Regression was applied to estimate parameters of different model forms/structures and using different excitation levels. The generated models are utilized to reconfigure a Nonlinear Dynamic Inversion (NDI) controller considering different testing conditions: normal, failure, and learning flights. Finally,an intelligent input design technique is proposed which enables autonomous identification of the vehicle’s response modal frequencies and emphasizes excitation power accordingly."],"dc:identifier.uri":["https://hdl.handle.net/10355/68835"],"dc:language.iso":["en_US"],"dc:publisher":["University of Missouri -- Kansas City"],"dc:title":["In-Flight Learning Based Flight Control of an Unmanned Aircraft System"],"dc:type":["Thesis"],"thesis:degree_discipline":["Engineering (UMKC)","Electrical and Computer Engineering (UMKC)"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Ph.D. (Doctor of Philosophy)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:17:53Z"}