The thesis documents the expansion of model reference adaptive control (MRAC) research previously developed at Wichita State University. This research was conducted in response to the National Aeronautics and Space Administration’s Integrated Resilient Aircraft Control project. The project seeks to develop new types of flight control systems that have the ability to react to unforeseen changes in the aircraft or its environment. Desktop simulations conducted have shown the ability to meet the results desired from the project. A desktop simulation for a six degree of freedom model of a Hawker Beechcraft Bonanza is modified with multiple MRAC architectures. These architectures include a model follower and proportional derivative and proportional integral controllers. In addition, an artificial neural network is used to compensate for modeling error and changes in the aircraft or the environment. The adaptive elements within each artificial neural network range from simplified bias only neural networks to linear basis vectors with additional modification terms. Each architecture was simulated to determine the error between the desired and actual aircraft state. Further analysis was conducted to determine time delay margin within each control loop. Finally, a comparison of architectures was conducted to determine which controller would be suited for flight testing on the Hawker Beechcraft Bonanza testbed.