  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 wide 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 main contribution is the development of a
generalized framework for the application of DNN-MRFT to higher-order systems.
One of the notable advantages of DNN-MRFT is 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. A
secondary 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 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 without any further tuning or
calibration the UAV would be able to pass through a vertical window, and
accurately follow trajectories achieving state-of-the-art performance. Such
demonstrated accuracy, speed, and robustness of identification pushes the
limits of state-of-the-art in real-time identification of UAVs.
