Summaries - Office of Research & Innovation
Research Summaries
Back Hardware Implementable Neural Circuit for Maximizing Collection Capacity of CMG Imaging Spacecraft
Fiscal Year | 2021 |
Division | Graduate School of Engineering & Applied Science |
Department | Mechanical & Aerospace Engineering |
Investigator(s) |
Proulx, Ronald J.
Karpenko, Mark |
Sponsor | Department of Defense Space (DoD) |
Summary | The objective of this project is to utilize machine-learning/AI-based concepts for increasing the agility of CMG attitude control systems. As part of this project, we intend to develop a hardware implementable, reconfigurable, recurrent network circuits. The neural circuits can be implemented as part of an integrated CMG momentum control system to enhance image collection activities by providing more images on a given pass. |
Keywords | |
Publications | Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal |
Data | Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal |