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

Low cost real-time identification of multirotor unmanned aerial vehicle (UAV) dynamics is an active area of research supported by the surge in demand and emerging application domains. Such real-time identification capabilities shorten development time and cost, making UAVs' technology more accessible, and enable a variety of advanced applications. In this paper, we present a novel comprehensive approach, called DNN-MRFT, for real-time identification and tuning of multirotor UAVs using the Modified Relay Feedback Test (MRFT) and Deep Neural Networks (DNN). The first contribution is the development of a generalized framework for the application of DNN-MRFT to higher-order systems. The second contribution is a method for the exact estimation of identified process gain which mitigates the inaccuracies introduced due to the use of the describing function method in approximating the response of Lure's systems. The third contribution is a generalized controller based on DNN-MRFT that takes-off a UAV with unknown dynamics and identifies the inner loops dynamics in-flight. Using the developed generalized framework, DNN-MRFT is sequentially applied to the outer translational loops of the UAV utilizing in-flight results obtained for the inner attitude loops. DNN-MRFT takes on average 15 seconds to get the full knowledge of multirotor UAV dynamics and was tested on multiple designs and sizes. The identification accuracy of DNN-MRFT is demonstrated by the ability of a UAV to pass through a vertical window without any further tuning, calibration, or feedforward terms. Such demonstrated accuracy, speed, and robustness of identification pushes the limits of state-of-the-art in real-time identification of UAVs.