  This research paper delves into the field of quadrotor dynamics, which are
famous by their nonlinearity, under-actuation, and multivariable nature. Due to
the critical need for precise modeling and control in this context we explore
the capabilities of NARX (Nonlinear AutoRegressive with eXogenous inputs)
Neural Networks (NN). These networks are employed for comprehensive and
accurate modeling of quadrotor behaviors, take advantage of their ability to
capture the hided dynamics. Our research encompasses a rigorous experimental
setup, including the use of PRBS (Pseudo-random binary sequence) signals for
excitation, to validate the efficacy of NARX-NN in predicting and controlling
quadrotor dynamics. The results reveal exceptional accuracy, with fit
percentages exceeding 99% on both estimation and validation data. Moreover, we
identified the quadrotor dynamics using different NARX NN structures, including
the NARX model with a sigmoid NN, NARX feedforward NN, and cascade NN. In
summary, our study positions NARX-NN as a transformative tool for quadrotor
applications, ranging from autonomous navigation to aerial robotics, thanks to
their accurate and comprehensive modeling capabilities.
