Feedback control for counterflow thrust vectoring with a turbine engine: Experiment design and robust control design and implementation

Engineering research over the last few years has successfully demonstrated the potential of thrust vector control using counterflow at conditions up to Mach 2. Flow configurations that include the pitch vectoring of rectangular jets and multi-axis vector control in diamond and axisymmetric nozzle geometries have been studied. Although bistable (on-off) fluid-based control has been around for some time, the present counterflow thrust vector control is unique because proportional and continuous jet response can be achieved in the absence of moving parts, while avoiding jet attachment, which renders most fluidic approaches unacceptable for aircraft and missile control applications. However, before this study, research had been limited to open-loop studies of counterflow thrust vectoring.; For practical implementation it was vital that the counterflow scheme be used in conjunction with feedback control. Hence, the focus of this research was to develop and experimentally demonstrate a feedback control design methodology for counterflow thrust vectoring.; This research focused on 2-D (pitch) thrust vectoring and addresses four key modeling issues. The first issue is to determine the measured variable to be commanded since the thrust vector angle is not measurable in real time. The second related issue is to determine the static mapping from the thrust vector angle to this measured variable. The third issue is to determine the dynamic relationship between the measured variable and the thrust vector angle. The fourth issue is to develop dynamic models with uncertainty characterizations.; The final and main goal was the design and implementation of robust controllers that yield closed-loop systems with fast response times, and avoid overshoot in order to aid in the avoidance of attachment. These controllers should be simple and easy to implement in real applications. Hence, PID design has been chosen. Robust control design is accomplished by using ℓ1 control theory in conjunction with the Popov-Tsypkin multiplier. The resulting optimization problem was solved using a real coded genetic-algorithm.