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HabSim-HMS: A Testbed for Investigating Situational Awareness and Autonomy for ExtraTerrestrial Habitat SystemsDeep-space habitation presents unique challenges in operating complex control-based systems in distant and disruptive environments far from Earth. The capability to handle unforeseen situations and manage system health is crucial for resilient operations in deep space. Trusted health management strategies operational on the International Space Station (ISS) heavily rely on ground control support. These strategies must adapt to cope with sizable communication delays imposed by the infrastructure limitations or light distances that separate ground control and the habitat. Alternative approaches that increase reliance on artificial intelligence-based onboard autonomy are constrained by limited onboard computing and risks associated with human-system integration. There is an increased need to study the design and architecture of the health management system (HMS) of such complex systems to address autonomy challenges for deep-space habitation. This paper presents a model-based HMS testbed developed at the Resilient Extra-Terrestrial Habitats Institute that is part of the HabSim, a system-of-systems simulator of a smart habitat. The testbed is designed to facilitate performance assessment of critical health management functions essential to enable fault-management autonomy, situational awareness, and interface-based mission control. In this paper, the HMS testbed is motivated and placed within HabSim, the architecture of its components is detailed, and the testbed features are demonstrated through an illustrative example. The lessons learned in developing this testbed that may be useful for others seeking to solve similar problems are also discussed.
Document ID
20250002974
Acquisition Source
2230 Support
Document Type
Accepted Manuscript (Version with final changes)
Authors
R Murali Krishnan ORCID
(Purdue University West Lafayette West Lafayette, United States)
Zixu Zhang
(Purdue University West Lafayette West Lafayette, United States)
Kairui Hao
(Purdue University West Lafayette West Lafayette, United States)
Sreehari Manikkan
(Purdue University West Lafayette West Lafayette, United States)
Paul Parsons
(Purdue University West Lafayette West Lafayette, United States)
Shirley J Dyke
(Purdue University West Lafayette West Lafayette, United States)
Ilias Bilionis ORCID
(Purdue University West Lafayette West Lafayette, United States)
Jiachen Wang
(University of Connecticut Storrs, Connecticut, United States)
Chuanyu Xue
(University of Connecticut Storrs, Connecticut, United States)
Song Han ORCID
(University of Connecticut Storrs, Connecticut, United States)
Mohsen Azimi
(Mississippi State University Starkville, United States)
Date Acquired
March 24, 2025
Publication Date
October 31, 2024
Publication Information
Publication: AIAA Journal
Publisher: American Institute of Aeronautics and Astronautics
Volume: 63
Issue: 2
Issue Publication Date: February 1, 2025
ISSN: 0001-1452
e-ISSN: 1533-385X
Subject Category
Cybernetics, Artificial Intelligence and Robotics
Funding Number(s)
CONTRACT_GRANT: 80NSSC19K1076
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
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