Achieving agile autonomous flight by an insect-sized micro aerial vehicle (MAV) will require improved technology that is radically smaller, lighter, and more power-efficient. One animal that has solved the problem is the fly, a virtuoso among insect flyers whose nervous system can perform sophisticated aerial maneuvers under severe computational constraints. This thesis is concerned with understanding and emulating the dynamics of the fly's feedback control system. Because vision is noisy and information rich, processing time may a problem for a fast-moving MAV or fly. By tracking the fruit fly Drosophila melanogaster in free flight in gusts of wind, I found that they incorporate feedback from wind-sensing antennae in a fast feedback loop that dampens the forward-flight dynamics. The slower dynamics are easier to control for long-delay visual feedback, making the fly more robust to the limitations of its visual system. Using the fly as inspiration, I designed a minimal, visual autocorrelation based controller that used a small array of visual sensors to stabilize a fan-actuated hovercraft robot in a narrow corridor. Using a model for correlators developed for the robot, I showed that a uniform array of visual correlators was sufficient to explain the free-flight velocity regulation behavior of flies, rather than a different model. In addition to illustrating the benefits of concurrent scientific analysis and engineering synthesis, the results give new insight into how to control small biological and man-made flying vehicles using limited, noisy sensors
