Acceleration Based Iterative Learning Control for Pugachev's Cobra Maneuver with Quadrotor Tail-sitter VTOL UAVs

This paper considers the well-known control problem of Pugachev's Cobra maneuver, a dramatic and demanding maneuver requiring the aircraft to fly at extremely high Angle of Attacks (AOA) where stalling occurs. We present a simple yet very effective feedback-iterative learning control structure to regulate the altitude error during the maneuver. Both the feedback controller and the iterative learning feed-forward 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, 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.