An Experimental Study of Concurrent Methods for Adaptively Controlling Vertical Tail Buffet in High Performance Aircraft
Roberts, Patrick James
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High performance twin-tail aircraft, like the F-15 and F/A-18, encounter a condition known as tail buffet. At high angles of attack, vortices are generated at the wing fuselage interface (shoulder) or other leading edge extensions. These vortices are directed toward the twin vertical tails. When the flow interacts with the vertical tail it creates pressure variations that can oscillate the vertical tail assembly. This results in fatigue cracks in the vertical tail assembly that can decrease the fatigue life and increase maintenance costs. For many years, research has been conducted to understand this phenomenon of buffet and to reduce its adverse effects on the fatigue life of aerospace structures. Many proposed solutions to this tail buffet problem have had limited success. These include strengthening the tail, modifying the vortex flow, using an active rudder control, and leading edge extensions. Some of the proposed active controls include piezoelectric actuators. Recently, an offset piezoceramic stack actuator was used on an F-15 wind tunnel model to control buffet induced vibrations at high angles of attack. The controller was based on acceleration feedback control methods. In this thesis a procedure for designing the offset piezoceramic stack actuators is developed. This design procedure includes determining the quantity and type of piezoceramic stacks used in these actuators. The changes of stresses, in the vertical tail caused by these actuators during an active control, are investigated. In many cases, linear controllers are very effective in reducing vibrations. However, during flight, the natural frequencies of the vertical tail structural system changes as the airspeed increases. This in turn, reduces the effectiveness of a linear controller. Other causes such as the unmodeled dynamics and nonlinear effects due to debonds also reduce the effectiveness of linear controllers. In this thesis, an adaptive neural network is used to augment the linear controller to correct these effects.