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Vision based control of Unmanned Aerial Vehicles (UAVs) has been adopted by awide range of applications due to the availability of low-cost on-board sensorsand computers. Tuning such systems to work properly requires extensive domainspecific experience, which limits the growth of emerging applications.Moreover, obtaining performance limits of UAV based visual servoing isdifficult due to the complexity of the models used. In this paper, we propose anovel noise tolerant approach for real-time identification and tuning of visualservoing systems, based on deep neural networks (DNN) classification of systemresponse generated by the modified relay feedback test (MRFT). The proposedmethod, called DNN with noise protected MRFT (DNN-NP-MRFT), can be used with amultitude of vision sensors and estimation algorithms despite the high levelsof sensor's noise. Response of DNN-NP-MRFT to noise perturbations isinvestigated and its effect on identification and tuning performance isanalyzed. The proposed DNN-NP-MRFT is able to detect performance changes due tothe use of high latency vision sensors, or due to the integration of inertialmeasurement unit (IMU) measurements in the UAV states estimation. Experimentalidentification closely matches simulation results, which can be used to explainsystem behaviour and predict the closed loop performance limits for a givenhardware and software setup. We also demonstrate the ability of DNN-NP-MRFTtuned UAVs to reject external disturbances like wind, or human push and pull.Finally, we discuss the advantages of the proposed DNN-NP-MRFT visual servoingdesign approach compared with other approaches in literature.

Noise Tolerant Identification and Tuning Approach Using Deep Neural Networks For Visual Servoing Applications