Kemik, H.Dal, M.B.Oniz, Y.2024-07-182024-07-18202197830305115552194-5357https://doi.org/10.1007/978-3-030-51156-2_126https://hdl.handle.net/11411/6193International Conference on Intelligent and Fuzzy Systems, INFUS 2020 -- 21 July 2020 through 23 July 2020 -- -- 242349In 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.eninfo:eu-repo/semantics/closedAccessAircraft DetectionAntennasFuzzy İnferenceFuzzy LogicFuzzy Neural NetworksLearning AlgorithmsPredictive Control SystemsSliding Mode ControlTrajectoriesUnmanned Aerial Vehicles (Uav)Adaptive Neuro-FuzzyConventional PidFuzzy Logic ControllersNeuro-Fuzzy ControllerReal-Time ExperimentReference TrajectoriesTrajectory TrackingTrajectory Tracking ControlControllersConference Object2-s2.0-8508875226710.1007/978-3-030-51156-2_1261091N/A10841197 AISC