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1 April 2016 Stochastic global identification of a bio-inspired self-sensing composite UAV wing via wind tunnel experiments
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In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bioinspired self-sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self-sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real-time information on the coupled air flow-structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the "global" coupled airflow-structural dynamics of the wing and their correlation with dynamic utter and stall. The obtained results demonstrate the successful system-level integration and effectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of "fly-by-fee" UAVs.
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Fotios Kopsaftopoulos, Raphael Nardari, Yu-Hung Li, Pengchuan Wang, and Fu-Kuo Chang "Stochastic global identification of a bio-inspired self-sensing composite UAV wing via wind tunnel experiments", Proc. SPIE 9805, Health Monitoring of Structural and Biological Systems 2016, 98051V (1 April 2016);

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