Trajectory Tracking of a Quadcopter Using Adaptive Neuro-Fuzzy Controller with Sliding Mode Learning Algorithm

Basit Öğe Kaydı
dc.authorscopusid57218311661
dc.authorscopusid57220433606
dc.authorscopusid23980961100
dc.contributor.authorKemik, H.
dc.contributor.authorDal, M.B.
dc.contributor.authorOniz, Y.
dc.date.accessioned2024-07-18T20:16:37Z
dc.date.available2024-07-18T20:16:37Z
dc.date.issued2021
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems, INFUS 2020 -- 21 July 2020 through 23 July 2020 -- -- 242349en_US
dc.description.abstractIn this work, the trajectory tracking control of an unmanned aerial vehicle (UAV) has been accomplished using adaptive neuro-fuzzy controllers. The update rules of the proposed controller have been derived based on the sliding mode control theory, where a sliding surface has been generated utilizing the parameters of the neuro-fuzzy controller to direct the error towards zero in a stable manner. To assess the effectiveness of the proposed control scheme, Parrot AR.Drone 2.0 has been utilized as the test platform, on which conventional PID and fuzzy logic controllers have been also implemented to provide means for comparing the performance of the proposed controller. Different reference trajectories have been generated for the real-time experimental studies, in which the discrepancies from these trajectories are used to determine the input signals to be applied to the proposed controllers. The analytical claims have been justified by the obtained results from the real-time experiments in the presence of large nonzero initial errors. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-51156-2_126
dc.identifier.endpage1091en_US
dc.identifier.isbn9783030511555
dc.identifier.issn2194-5357
dc.identifier.scopus2-s2.0-85088752267en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1084en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-51156-2_126
dc.identifier.urihttps://hdl.handle.net/11411/6193
dc.identifier.volume1197 AISCen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofAdvances in Intelligent Systems and Computingen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAircraft Detectionen_US
dc.subjectAntennasen_US
dc.subjectFuzzy İnferenceen_US
dc.subjectFuzzy Logicen_US
dc.subjectFuzzy Neural Networksen_US
dc.subjectLearning Algorithmsen_US
dc.subjectPredictive Control Systemsen_US
dc.subjectSliding Mode Controlen_US
dc.subjectTrajectoriesen_US
dc.subjectUnmanned Aerial Vehicles (Uav)en_US
dc.subjectAdaptive Neuro-Fuzzyen_US
dc.subjectConventional Piden_US
dc.subjectFuzzy Logic Controllersen_US
dc.subjectNeuro-Fuzzy Controlleren_US
dc.subjectReal-Time Experimenten_US
dc.subjectReference Trajectoriesen_US
dc.subjectTrajectory Trackingen_US
dc.subjectTrajectory Tracking Controlen_US
dc.subjectControllersen_US
dc.titleTrajectory Tracking of a Quadcopter Using Adaptive Neuro-Fuzzy Controller with Sliding Mode Learning Algorithmen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

Scopus Indexed Publications