  The Pugachev's cobra maneuver is a dramatic and demanding maneuver requiring
the aircraft to fly at extremely high Angle of Attacks (AOA) where stalling
occurs. This paper considers this maneuver on tail-sitter UAVs. We present a
simple yet very effective feedback-iterative learning position control
structure to regulate the altitude error and lateral displacement during the
maneuver. Both the feedback controller and the iterative learning controller
are based on the aircraft acceleration model, which is directly measurable by
the onboard accelerometer. Moreover, the acceleration model leads to an
extremely simple dynamic model that does not require any model identification
in designing the position controller, greatly simplifying the implementation of
the iterative learning control. Real-world outdoor flight experiments on the
"Hong Hu" UAV, an aerobatic yet efficient quadrotor tail-sitter UAV of
small-size, are provided to show the effectiveness of the proposed controller.
