2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Dec. 19, 2016 to Dec. 21, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2016.066
System identification is an established technique for obtaining a reliable mathematical model of an Unmanned Aerial Vehicle (UAV) using aircraft input output data. In this research, a unique three step procedure is presented for system identification of small scale fixed wing UAV. Flight experiment was conducted with specifically designed maneuvers to identify lateral-directional dynamic model of the aircraft. Initial reference model was developed using United States Data Compendium (DATCOM) and grey box identification was performed in Matlab® system identification toolbox. Model parameters were estimated using Prediction Error Method (PEM) and the estimated parameters were improved iteratively through Adaptive Gauss Newton (GNA) optimization. Model validation was performed and the UAV's aerodynamic coefficients were determined of the UAV. Excellent validation results show the usefulness of the three step identification scheme. The identified lateral model can be practically used for various applications including heading control and autonomous navigation of the UAV.
Mathematical model, Atmospheric modeling, Aerodynamics, Aircraft, Adaptation models, System identification, Data models,aerodynamic coefficients, Unmanned Aerial Vehicle (UAV), system identification, lateral dynamics, DATCOM, grey box model, Prediction Error Method (PEM), Adaptive Gauss Newton (GNA)
Jabran Ahsan, Mansoor Ahsan, Anis Jamil, Ahsan Ali, "Grey Box Modeling of Lateral-Directional Dynamics of a UAV through System Identification", 2016 International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 324-329, 2016, doi:10.1109/FIT.2016.066