Date of Award
Master of Science in Aeronautical Engineering
Department of Aeronautics and Astronautics
Christopher M Shearer, PhD
This study was motivated by a need to develop a reliable method of predicting the agility characteristics of various aircraft. To fully investigate the agility of an aircraft, maneuvers which push the limits of an aircraft’s maneuvering capabilities must be simulated. In these cases, classic trajectory optimization techniques either require too many assumptions for a realistic solution or require a good guess of the final solution before the problem is even attempted. This study investigated both the utility of pseudospectral optimization methods for robust trajectory optimization as well as the potential for demonstrating differences in aircraft agility characteristics of several specific maneuvers. Building off of a pseudospectral optimization software package named DIDO, a robust maneuver definition and trajectory optimization system was developed to simulate various maneuvers specifically designed to demonstrate aircraft maneuvering limits. This system was used to optimize the trajectories of three variations of a baseline F-16 mathematical model developed to simulate important differences in aircraft agility characteristics. Initial results showed significant instabilities in the interface between the mathematical model and the optimization scheme. These instabilities were mitigated through modifications of the system’s cost function and the resulting trajectories demonstrated the relative advantages which can be created by subtle differences in aircraft designs. Future work in this area should include further refinement of the driving cost function and creation of a graphical user interface to simplify the maneuver definition process. The resulting system could be highly useful in other trajectory optimization research as well as non-related areas such as accident investigation and reverse engineering.
DTIC Accession Number
Hall, David M., "Demonstrative Maneuvers for Aircraft Agility Predictions" (2008). Theses and Dissertations. 2681.