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Low cost real-time identification of multirotor unmanned aerial vehicle (UAV)dynamics is an active area of research supported by the surge in demand andemerging application domains. Such real-time identification capabilitiesshorten development time and cost, making UAVs' technology more accessible, andenable a wide variety of advanced applications. In this paper, we present anovel comprehensive approach, called DNN-MRFT, for real-time identification andtuning of multirotor UAVs using the Modified Relay Feedback Test (MRFT) andDeep Neural Networks (DNN). The main contribution is the development of ageneralized framework for the application of DNN-MRFT to higher-order systems.One of the notable advantages of DNN-MRFT is the exact estimation of identifiedprocess gain, which mitigates the inaccuracies introduced due to the use of thedescribing function method in approximating the response of Lure's systems. Asecondary contribution is a generalized controller based on DNN-MRFT thattakes-off a UAV with unknown dynamics and identifies the inner loops dynamicsin-flight. Using the developed framework, DNN-MRFT is sequentially applied tothe outer translational loops of the UAV utilizing in-flight results obtainedfor the inner attitude loops. DNN-MRFT takes on average 15 seconds to get thefull knowledge of multirotor UAV dynamics and without any further tuning orcalibration the UAV would be able to pass through a vertical window, andaccurately follow trajectories achieving state-of-the-art performance. Suchdemonstrated accuracy, speed, and robustness of identification pushes thelimits of state-of-the-art in real-time identification of UAVs.

Multirotors from Takeoff to Real-Time Full Identification Using the Modified Relay Feedback Test and Deep Neural Networks