Despite all the research being done in an attempt to bridge the gap between control systems and artificial intelligence, there is still an immense risk of failure and instability that exists. One particular application that this research will look into and expand on is aircraft control mechanisms. This article will examine the existing uncertainties within these systems that could be suspected as the cause of failure in the artificial control operation of an aircraft. This study will act as a further extension of research on the feedback linearization of an aircraft's control architecture using adaptive neural networks to decrease the probability of an uncontrolled error resulting from the nonlinearity of the aircraft's dynamic characteristics. The stability of previously implemented mechanisms to control aircraft systems will also be investigated. This research will require a thorough approach and understanding of various possible areas of malfunction and instability caused by multiple factors, including, external interferences and inefficiencies that accumulate within the controller that can mislead or cause an undesirable effect on the system. Examining similar areas where this study may be used for further research, while also discussing opportunities to apply these procedures to relatable applications will be analyzed, as it is of key importance for the progression of this technology.