RIFAT HACIOĞLU, AYTAÇ ALTAN, EGEMEN BELGE, HIZIR KAAN KABA, AHMET PARLAK

Estimation of Small Unmanned Aerial Vehicle Lateral Dynamic Model with System Identification Approaches

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.

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[1] A. Loya, M. Duraid, K. Maqsood, R. R. Khan. “The implementation and lateral control optimization of a UAV based on phase lead compensator and signal constraint controller.” Engineering, vol. 10. 10, 2018.
[2] P. D. Jameson, A. Cooke, “Developing Real-time System Identification for UAVs.” 2012 UKACC International Conference on Control, 2012.
[3] W. Saengphet, S. Tantrairatn, C. Thumtae, J. Srisertpol, “Implementation of System Identification and Flight Control System for UAV.” 3rd International and Flight Control System for UAV, 2017.
[4] A. Dorobantu, A. Murch, B. Mettler, G. Balas. ”System identification for small, low-cost, fixed-wing unmanned aircraft.” Journal of Aircraft, vol. 50. 4, 2013, pp.1117-1130.
[5] V. Puttige, S. Anavatti. “Real-time system identification of unmanned aerial vehicles: A multi-network approach.” Journal of Computers, vol. 3.7, 2008.
[6] W. Wei, M. B. Tischler, N. Schwartz, K. Cohen. “System identification and flight control of an unmanned quadrotor.” Advanced UAV Aerodynamics, Flight Stability and Control: Novel Concepts, Theory and Applications, 2017, pp.695-727.
[7] A. Altan, R. Hacıoğlu. “Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances.” Mechanical Systems and Signal Processing, 2020.
[8] A. Altan, R. Hacıoğlu, “Modeling of Three-axis Gimbal System on Unmanned Air Vehicle (UAV) under External Disturbances.” 2017 25th Signal Processing and Communications Applications Conference (SIU), 2017.
[9] A. Altan, Ö. Aslan, R. Hacıoğlu, “Model Predictive Control of Load Transporting System on Unmanned Aerial Vehicle (UAV).” Fifth International Conference on Advances in Mechanical and Robotics Engineering, 2017.
[10] Y. H. Aleed, T. A. Tutunji, “RC Helicopter Modeling Using Re-engineering and System Identification.” 14th International Multi-Conference on Systems, Signals & Devices (SSD), 2017.
[11] C. Dube, J. O. Pedro. “Modelling and closed-loop system identification of a quadrotor-based aerial manipulator.” Journal of Physics: Conference Series, vol. 1016.1, 2018.
[12] J. E. Sierra, M. Santos. “Modelling engineering systems using analytical and neural techniques: Hybridization.” Neurocomputing, 2018, pp. 70-83.
[13] A. E. Ahmed, A. Hafez, A. N. Ouda, H. E. H. Ahmed, H. M. Abd-Elkader. “Modeling of a small unmanned aerial vehicle.” International Journal of Aerospace and Mechanical Engineering, vol. 9. 3, 2015.
[14] L. Ljung, System Identification, System Identification Theory for The Users, 1999, p. 609.