Flight testing and modeling techniques were developed to accurately identify global nonlinear aerodynamic models for aircraft in real time. The techniques were developed and demonstrated during flight testing of a remotely-piloted subscale propeller-driven fixed-wing aircraft using flight test maneuvers designed to simulate a Learn-To-Fly scenario. Prediction testing was used to evaluate the quality of the global models identified in real time. The real-time global nonlinear aerodynamic modeling algorithm will be integrated and further tested with learning adaptive control and guidance for NASA Learn-To-Fly concept flight demonstrations.
Real-Time Global Nonlinear Aerodynamic Modeling for Learn-To-Fly
2016 AIAA SciTech Conference ; 2016 ; San Diego, CA, United States
2016-01-04
Aufsatz (Konferenz)
Keine Angabe
Englisch
Real-Time Global Nonlinear Aerodynamic Modeling for Learn-To-Fly (AIAA 2016-2010)
British Library Conference Proceedings | 2016
|