The development of atmospheric entry guidance methods is crucial to achieving the requirements for future missions to Mars; however, many missions implement a unique controller which are spacecraft specific. Here we look at the implementation of neural networks as a baseline controller that will work for a variety of different spacecraft. To accomplish this, a simulation is developed and validated with the Apollo controller. A feedforward neural network controller is then analyzed and compared to the Apollo case.
Bachelor of Science in Mechanical Engineering (B.S.M.E.)
College of Engineering and Computer Science
Mechanical and Aerospace Engineering
Orlando (Main) Campus
Length of Campus-only Access
Martin, Dillon A., "Atmospheric Entry" (2017). Honors Undergraduate Theses. 354.
Restricted to the UCF community until 6-1-2019; it will then be open access.