System Identification and Modeling of Actuation Dynamics for Fixed-Wing UAVs Using Empirical Servo Data

The actuation system of fixed-wing Unmanned Aerial Vehicles (UAVs) is essential for sustaining flight stability and control by manipulating control surfaces, including the elevator, ailerons, and rudder. Servomechanisms that rely on inputs from either a remote operator or an onboard autopilot control these modifications. To guarantee optimal tracking and effective control, it is essential that the actuator's actual output (deflection angle) closely align with the requested input. Nonetheless, actual actuators display nonlinearities, time lags, and dynamic characteristics that may impair system performance if inadequately modelled. This article presents a system identification of the actuation system of a fixed-wing UAV using a Corona servo to regulate flight surface deflections. Input-output data from the servomechanism were collected to evaluate several mathematical models that characterize the system's dynamics. The identification method included data collecting, model structure selection, parameter estimates, and model validation. The outcomes facilitate enhanced simulation accuracy, predictive control development, and fault detection proficiency. This modelling methodology eventually improves the reliability and accuracy of UAV flight control systems. The technique illustrates how empirical data may enhance theoretical modelling in aeronautical applications.