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Linear systems can be used to adequatelymodel and control an aircraft in either ideal steady-level flight or in ideal hovering flight. However, constructing a single unified system capable of adequately modeling or controlling an airplane in steady-level flight and in hovering flight, as well as during the highly nonlinear transitions between the two, requires the use of more complex systems, such as scheduled-linear, nonlinear, or stable adaptive systems. This paper discusses the use of dynamic inversionwith real-time neural network adaptation as ameans to provide a single adaptive controller capable of controlling a fixed-wing unmanned aircraft system in all three flight phases: steady-level flight, hovering flight, and the transitions between them. Having a single controller that can achieve and transition between steady-level and hovering flight allows utilization of the entire low-speed flight envelope, even beyond stall conditions. This method is applied to the GTEdge, an eight-foot wingspan, fixed-wing unmanned aircraft system that has been fully instrumented for autonomous flight. This paper presents data from actual flight-test experiments in which the airplane transitions from high-speed, steady-level flight into a hovering condition and then back again. Nomenclature Â1, Â2, B ̂ = linearized vehicle dynamics a = acceleration or activation potential
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Johnson et al. (2008) studied this question.
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