Quadrotor Experimental Dynamic Identification with Comprehensive NARX Neural Networks

This research paper delves into the field of quadrotor dynamics, which are famous by their nonlinearity, underactuation, 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.