Date of Award
Master of Science in Aeronautical Engineering
Department of Aeronautics and Astronautics
Paul A. Blue, PhD
The United States Air Force has advanced fighter aircraft that lose the ability to operate in a large portion of their operating flight envelope when an air data system failure is experienced. These aircraft are reverted to a fixed set of standby-gains that limit their maneuverability, degrade handling qualities, and increase susceptibility to departure. The purpose of this research was to determine if three alternative methods of standby-gain-scheduling could provide robust control with minimal performance degradation despite the lack of air data. To accomplish this, three methods of standby-gain-scheduling were developed, integrated, and tested in the Infinity Cube simulator at the Air Force Research Laboratory/RBCD building. The first method improved upon an algorithm which used inertial data to estimate an aircraft's true velocity used to drive the gains in an F-16 controller. This algorithm was validated by post-processing high-fidelity simulator data and actual flight data. The second method simply used inertial velocities to drive the gains in an F-16 controller. The final method used a disturbance observer controller which controlled aircraft dynamics without the use of gain-scheduling. The results showed the potential for effective aircraft control with minimal performance degradation following an air data system failure. Potential benefits to this research include eliminating the need to make switch actuations to correctly schedule the standby-gains; improving aircraft performance when flying with standby-gains; allowing the pilot to continue with a combat mission instead of returning to base with an air data system failure; and helping contribute to the removal of Pitot tubes in an attempt to eliminate a failure mode and to reduce the radar cross section of an aerial vehicle.
DTIC Accession Number
Coldsnow, Matthew W., "Alternative Methods to Standby Gain Scheduling Following Air Data System Failure" (2009). Theses and Dissertations. 2410.