An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles
In this paper we develop initial offline and online capabilities for a self-aware aerospace vehicle. Such a vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings via sensors and responding intelligently. The key challenge to enabling su...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | en_US |
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Elsevier
2014
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| Online Access: | http://hdl.handle.net/1721.1/90631 https://orcid.org/0000-0002-5969-9069 https://orcid.org/0000-0003-2156-9338 |
| _version_ | 1826193684592328704 |
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| author | Chambers, J. Cowlagi, Raghvendra V. Kordonowy, D. Lecerf, M. Ulker, F. Allaire, Douglas L. Mainini, Laura Willcox, Karen E. |
| author2 | MIT-SUTD Collaboration Office |
| author_facet | MIT-SUTD Collaboration Office Chambers, J. Cowlagi, Raghvendra V. Kordonowy, D. Lecerf, M. Ulker, F. Allaire, Douglas L. Mainini, Laura Willcox, Karen E. |
| author_sort | Chambers, J. |
| collection | MIT |
| description | In this paper we develop initial offline and online capabilities for a self-aware aerospace vehicle. Such a vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings via sensors and responding intelligently. The key challenge to enabling such a self-aware aerospace vehicle is to achieve tasks of dynamically and autonomously sensing, planning, and acting in real time. Our first steps towards achieving this goal are presented here, where we consider the execution of online mapping strategies from sensed data to expected vehicle capability while accounting for uncertainty. Libraries of strain, capability, and maneuver loading are generated offline using vehicle and mission modeling capabilities we have developed in this work. These libraries are used dynamically online as part of a Bayesian classification process for estimating the capability state of the vehicle. Failure probabilities are then computed online for specific maneuvers. We demonstrate our models and methodology on decisions surrounding a standard rate turn maneuver. |
| first_indexed | 2024-09-23T09:43:03Z |
| format | Article |
| id | mit-1721.1/90631 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:43:03Z |
| publishDate | 2014 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/906312022-09-30T16:23:13Z An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles Chambers, J. Cowlagi, Raghvendra V. Kordonowy, D. Lecerf, M. Ulker, F. Allaire, Douglas L. Mainini, Laura Willcox, Karen E. MIT-SUTD Collaboration Office Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Allaire, Douglas L. Lecerf, M. Mainini, Laura Ulker, F. Willcox, Karen E. In this paper we develop initial offline and online capabilities for a self-aware aerospace vehicle. Such a vehicle can dynamically adapt the way it performs missions by gathering information about itself and its surroundings via sensors and responding intelligently. The key challenge to enabling such a self-aware aerospace vehicle is to achieve tasks of dynamically and autonomously sensing, planning, and acting in real time. Our first steps towards achieving this goal are presented here, where we consider the execution of online mapping strategies from sensed data to expected vehicle capability while accounting for uncertainty. Libraries of strain, capability, and maneuver loading are generated offline using vehicle and mission modeling capabilities we have developed in this work. These libraries are used dynamically online as part of a Bayesian classification process for estimating the capability state of the vehicle. Failure probabilities are then computed online for specific maneuvers. We demonstrate our models and methodology on decisions surrounding a standard rate turn maneuver. 2014-10-08T15:09:41Z 2014-10-08T15:09:41Z 2013-01 Article http://purl.org/eprint/type/JournalArticle 18770509 http://hdl.handle.net/1721.1/90631 Allaire, D., J. Chambers, R. Cowlagi, D. Kordonowy, M. Lecerf, L. Mainini, F. Ulker, and K. Willcox. “An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles.” Procedia Computer Science 18 (January 2013): 1959–1968. https://orcid.org/0000-0002-5969-9069 https://orcid.org/0000-0003-2156-9338 en_US http://dx.doi.org/10.1016/j.procs.2013.05.365 Procedia Computer Science Creative Commons Attribution http://creativecommons.org/licenses/by-nc-nd/3.0/ application/pdf Elsevier Elsevier |
| spellingShingle | Chambers, J. Cowlagi, Raghvendra V. Kordonowy, D. Lecerf, M. Ulker, F. Allaire, Douglas L. Mainini, Laura Willcox, Karen E. An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles |
| title | An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles |
| title_full | An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles |
| title_fullStr | An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles |
| title_full_unstemmed | An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles |
| title_short | An Offline/Online DDDAS Capability for Self-Aware Aerospace Vehicles |
| title_sort | offline online dddas capability for self aware aerospace vehicles |
| url | http://hdl.handle.net/1721.1/90631 https://orcid.org/0000-0002-5969-9069 https://orcid.org/0000-0003-2156-9338 |
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