Flow Field Image Based Attitude Control for Small Unmanned Aerial Vehicles

In recent years, microscale flow sensors have been extensively studied and developed, which can measure local flow information such as pressure or wall-shear stress over aerial vehicle surfaces in real-time. It is expected that with those sensors onboard, SUAVs can potentially mimic birds or bats in achieving more stable and agile flights than purely relying on rigid body sensors. However, it is challenging to utilize such a rich amount of surface airflow information to enable agile SUAV flights, which could be in the form of 2-D or 3-D images. In this paper, a flow field image based approach is developed for SUAV attitude control. The proposed robust controllers work directly on flow field images through defined image operators. The asymptotically stability of controllers are proven for the closed-loop systems under bounded uncertainties. The effectiveness of the controllers are demonstrated in simulations for both pitching motion and three-axis attitude motion even under gust wind conditions.