Abstract
Modeling of unmanned aerial vehicle (UAV) with system identification is very important in terms of its model-based effective control. The modeling of UAV is required for aircraft crashes, analyzing autonomous aircrafts, preventing external disturbances, pre-flight analysis. However, since UAV has nonlinear inherent dynamics including inherent chaoticity and fractality, it becomes difficult to obtain a mathematical model under external disturbance. In this study, some of the inherent nonlinear dynamics of UAV are linearized and the model of UAV is obtained by system identification approaches under external disturbance. The linearized lateral dynamics of a fixed wing UAV is used in this study. Further, the flight motion equations applied to fixed wing UAV have been utilized for obtaining the coefficients of lateral model for straight and level flight. The roll angles are calculated using transfer functions for aileron, rudder and deflections inputs. The autoregressive exogenous (ARX), autoregressive moving average with exogenous (ARMAX) and output error (OE) parametric system identification approaches are performed to estimate UAV lateral dynamic system response as using empirical input-output data sets. The accuracy of parametric model estimation and model degrees are compared for different external disturbance effects.
Highlights
UNMANNED AERIAL VEHICLE (UAV) modeling has become an important research subject in recent years
The model parameters of UAV is obtained with system identification approaches including Autoregressive Exogenous (ARX), Autoregressive Moving Average with Exogenous Variable (ARMAX) and Output Error (OE) under different external disturbances
The lateral dynamics of fixed-wing UAV estimated through ARX, ARMAX and OE system identification models
Summary
UNMANNED AERIAL VEHICLE (UAV) modeling has become an important research subject in recent years. System identification techniques are frequently used in modeling of dynamic systems such as UAV [1]. The model parameters of UAV is obtained with system identification approaches including Autoregressive Exogenous (ARX), Autoregressive Moving Average with Exogenous Variable (ARMAX) and Output Error (OE) under different external disturbances. In [5], both online and offline models of nonlinear and complex UAV have been obtained using system identification procedure based on Artificial Neural Network (ANN). The difference of this study from studies in literature is that the parametric system identification techniques are used for modelling roll angle with aileron and rudder input in fixed wing UAV under external disturbances.
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