CONTROL-ORIENTED REDUCED ORDER MODELING OF

Flying insects achieve flight stabilization and control in a manner that re-quires only small, specialized neural structures to perform the essential components of sensing and feedback, achieving unparalleled levels of robust aerobatic flight on limited computational resources. An engineering mechanism to replicate these con-trol strategies could provide a dramatic increase in the mobility of small scale aerial robotics, but a formal investigation has not yet yielded tools that both quantita-tively and intuitively explain flapping wing flight as an “input-output ” relationship. This work uses experimental and simulated measurements of insect flight to cre-ate reduced order flight dynamics models. The framework presented here creates models that are relevant for the study of control properties. The work begins with automated measurement of insect wing motions in free flight, which are then used to calculate flight forces via an empirically-derived aerodynamics model. When paired with rigid body dynamics and experimentally measured state feedback, both the bare airframe and closed loop systems may be analyzed using frequency domain system identification. Flight dynamics models describing maneuvering about hove