A dynamic model parameter identification method for quadrotors using flight data

Abstract

The dynamic model parameter identification is important for unmanned aerial vehicle modeling and control. The unmanned aerial vehicle model parameters are usually identified through wind tunnel experiments, which are complex. In this paper, a model parameter identification method is proposed using the flight data for quadrotors. The parameters of the thrust, drag force, torque, rolling moment and pitching moment are estimated through Kalman filter. Global positioning system and inertial sensors are used as measurements. The observabilities of the model parameters and their degrees of observability are analyzed. Flight experiments are carried out to verify the proposed method. It is shown that the model parameters estimated by the proposed method have good accuracies, demonstrating the validity of the proposed method.