(Georgia Institute of Technology, 2008-03)
Johnson, Eric N.; Wu, Allen D.; Neidhoefer, James C.; Kannan, Suresh K.; Turbe, Michael A.
Linear systems can be used to adequately model 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 inversion with real-time neural network adaptation as a means 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.
(Georgia Institute of Technology, 2005-09)
Wu, Allen D.; Johnson, Eric N.; Proctor, Alison A.
Many onboard navigation systems use the Global Positioning System to bound the errors
that result from integrating inertial sensors over time. Global Positioning System information,
however, is not always accessible since it relies on external satellite signals. To this
end, a vision sensor is explored as an alternative for inertial navigation in the context of an
Extended Kalman Filter used in the closed-loop control of an unmanned aerial vehicle. The
filter employs an onboard image processor that uses camera images to provide information
about the size and position of a known target, thereby allowing the flight computer to derive
the target's pose. Assuming that the position and orientation of the target are known a priori,
vehicle position and attitude can be determined from the fusion of this information with inertial
and heading measurements. Simulation and flight test results verify filter performance
in the closed-loop control of an unmanned rotorcraft.