Flapping wing vehicles have become a compelling alternative to classical fixed or rotary wing aircraft, especially as unmanned aircraft technology focuses on smaller, more agile platforms. Flying insects provide an inspiration for the control of flapping wing platforms, using limited computational resources in their specialized neural pathways to generate robust, agile performance. The flapping wing design is less studied, and the underlying physical principles are often more complex – non-linear time varying dynamics are dominated by forcing due to complex, unsteady aerodynamics. Reduced order models are critical to formulating tractable sensing and control concepts from the complex physics of flapping wing flight. Previous research has focused on a single methodology for the estimation of flight dynamics. This dissertation investigates the reduced order modeling of flapping wing flight dynamics for the purposes of tractable simulation and control, comparing multiple methodologies. Simplification of rigid body vehicle dynamics due to both linearization and time-invariance is discussed, and computational and experimental verification is presented for a simplified model of flapping wing aerodynamics. Additionally, a novel method is presented to maximize the agility and performance of a flapping wing vehicle when reducing the number of control inputs. These reduced order modeling techniques are applied to both a model of a small flying insect and to a flapping wing micro air vehicle.