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A micro air vehicle (MAV) is a class of miniature unmanned aerial vehicles that has a size restriction and may be autonomous. Fixed-wing MAVs are very attractive for outdoor surveillance missions since they generally offer better payload and endurance capabilities than rotorcraft or flapping-wing vehicles of equal size. This research paper describes the methodology applying indicial function theory and artificial neural networks for identification of aerodynamic derivatives for fixed-wing MAV. The formulation herein proposed extends well- known aerodynamic theories, which are limited to thin aerofoils in incompressible flow, to strake wing planforms. Using results from dynamic water tunnel tests and indicial functions approach allowed to identify MAV aerodynamic derivatives. The experiments were conducted in a water tunnel in the course of dynamic tests of periodic oscillatory motion. The tests program range was set at high angles of attack and a wide scope of reduced frequencies of angular movements. Due to a built-in propeller, the model’s structure test program was repeated for a turned-on propelled drive system. As a result of these studies, unsteady aerodynamics characteristics and aerodynamic derivatives of the micro-aircraft were identified as functions of state parameters. At the Warsaw University of Technology and the Air Force Institute of Technology, a “Bee” fixed wings micro aerial vehicle with an innovative strake-wing outline and a propeller placed in the wing gap was worked. This article is devoted to the problems of identification of aerodynamic derivatives of this micro-aircraft. The result of this research was the identification of the aerodynamic derivatives of the fixed wing MAV “Bee” as non-linear functions of the angle of attack, and reduced frequency. The identification was carried out using the indicial function approach.

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