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Neural Net Control for Electric Propulsion in 3-Body Orbits

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC20C0139
Agency Tracking Number: 193159
Amount: $749,237.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: H9
Solicitation Number: SBIR_19_P2
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-07-10
Award End Date (Contract End Date): 2022-07-09
Small Business Information
2100 Central Avenue, Suite 102
Boulder, CO 80301-2887
United States
DUNS: 079689503
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nathan Parrish
 (720) 545-9191
 parrish@advancedspace.com
Business Contact
 Bradley Cheetham
Phone: (720) 545-9189
Email: cheetham@advancedspace.com
Research Institution
N/A
Abstract

The proposed innovation, neural networks (NNs) for electric propulsion (EP) mdash; NNEP, leverages the fundamental principles of optimal control (OC) and a rich field of recent advancements in the area of artificial intelligence to automate spacecraft maneuver correction, resulting in improved spacecraft maneuver accuracy, lowered operations complexity, and cost savings. NNs are used as function approximators, learning the complex relationship between spacecraft state and the costates defining the OC to return to a reference trajectory.nbsp;The NNEP technology builds on the variety of technologies that exist for onboard navigation (such as GPS, CAPS, or OpNav). Once the spacecraft has generated a state estimate, it evaluates a pre-trained NN to find the corresponding costates (non-physical terms created in the process of solving an OC problem). The NNEP technology maps state errors to costates because the costates are always smoothly-varying, even for non-smooth OC. Within seconds of the nav update, the spacecraft autonomously determines the control required for the next several days and checks for constraint violations.The NNEP technology consists of both a novel application of NNs to relevant astrodynamics problems and an architecture for implementing this technology in real operational environments and in flight software (FSW). A proof of concept of the technology was developed during Phase I, including demonstration of the building blocks for a FSWnbsp;implementation. Phase II funding will mature the technology further and result in a prototype FSW implementation running on representative space hardware.nbsp;Many research groups are now investigating the use of NNs to automate spacecraft trajectory corrections. NNEP combines Advanced Spacersquo;s practical institutional experience of mission design, navigation, and operations for a wide variety of cutting-edge space missions with the powerful theoretical advantages of NNs and OC.

* Information listed above is at the time of submission. *

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