Methodology for Dynamic Data-Driven Online Flight Capability Estimation
This paper presents a data-driven approach for the online updating of the flight envelope of an unmanned aerial vehicle subjected to structural degradation. The main contribution of the work is a general methodology that leverages both physics-based modeling and data to decompose tasks into two phas...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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American Institute of Aeronautics and Astronautics
2017
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| Online Access: | http://hdl.handle.net/1721.1/106347 https://orcid.org/0000-0003-2156-9338 |
| _version_ | 1826197467592392704 |
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| author | Allaire, Douglas Lecerf, Marc A. Willcox, Karen E |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Allaire, Douglas Lecerf, Marc A. Willcox, Karen E |
| author_sort | Allaire, Douglas |
| collection | MIT |
| description | This paper presents a data-driven approach for the online updating of the flight envelope of an unmanned aerial vehicle subjected to structural degradation. The main contribution of the work is a general methodology that leverages both physics-based modeling and data to decompose tasks into two phases: expensive offline simulations to build an efficient characterization of the problem and rapid data-driven classification to support online decision making. In the approach, physics-based models at the wing and vehicle level run offline to generate libraries of information covering a range of damage scenarios. These libraries are queried online to estimate vehicle capability states. The state estimation and associated quantification of uncertainty are achieved by Bayesian classification using sensed strain data. The methodology is demonstrated on a conceptual unmanned aerial vehicle executing a pullup maneuver, in which the vehicle flight envelope is updated dynamically with onboard sensor information. During vehicle operation, the maximum maneuvering load factor is estimated using structural strain sensor measurements combined with physics-based information from precomputed damage scenarios that consider structural weakness. Compared to a baseline case that uses a static as-designed flight envelope, the self-aware vehicle achieves both an increase in probability of executing a successful maneuver and an increase in overall usage of the vehicle capability. |
| first_indexed | 2024-09-23T10:48:07Z |
| format | Article |
| id | mit-1721.1/106347 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:48:07Z |
| publishDate | 2017 |
| publisher | American Institute of Aeronautics and Astronautics |
| record_format | dspace |
| spelling | mit-1721.1/1063472022-09-30T23:08:30Z Methodology for Dynamic Data-Driven Online Flight Capability Estimation Allaire, Douglas Lecerf, Marc A. Willcox, Karen E Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Lecerf, Marc A. Willcox, Karen E This paper presents a data-driven approach for the online updating of the flight envelope of an unmanned aerial vehicle subjected to structural degradation. The main contribution of the work is a general methodology that leverages both physics-based modeling and data to decompose tasks into two phases: expensive offline simulations to build an efficient characterization of the problem and rapid data-driven classification to support online decision making. In the approach, physics-based models at the wing and vehicle level run offline to generate libraries of information covering a range of damage scenarios. These libraries are queried online to estimate vehicle capability states. The state estimation and associated quantification of uncertainty are achieved by Bayesian classification using sensed strain data. The methodology is demonstrated on a conceptual unmanned aerial vehicle executing a pullup maneuver, in which the vehicle flight envelope is updated dynamically with onboard sensor information. During vehicle operation, the maximum maneuvering load factor is estimated using structural strain sensor measurements combined with physics-based information from precomputed damage scenarios that consider structural weakness. Compared to a baseline case that uses a static as-designed flight envelope, the self-aware vehicle achieves both an increase in probability of executing a successful maneuver and an increase in overall usage of the vehicle capability. United States. Air Force Office of Scientific Research. Dynamic Data-Driven Application Systems Program (Grant FA9550-11-1-0339) 2017-01-11T21:09:29Z 2017-01-11T21:09:29Z 2015-10 2015-04 Article http://purl.org/eprint/type/JournalArticle 0001-1452 1533-385X http://hdl.handle.net/1721.1/106347 Lecerf, Marc, Douglas Allaire, and Karen Willcox. “Methodology for Dynamic Data-Driven Online Flight Capability Estimation.” AIAA Journal 53.10 (2015): 3073–3087. https://orcid.org/0000-0003-2156-9338 en_US http://dx.doi.org/10.2514/1.j053893 AIAA Journal Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Institute of Aeronautics and Astronautics MIT web domain |
| spellingShingle | Allaire, Douglas Lecerf, Marc A. Willcox, Karen E Methodology for Dynamic Data-Driven Online Flight Capability Estimation |
| title | Methodology for Dynamic Data-Driven Online Flight Capability Estimation |
| title_full | Methodology for Dynamic Data-Driven Online Flight Capability Estimation |
| title_fullStr | Methodology for Dynamic Data-Driven Online Flight Capability Estimation |
| title_full_unstemmed | Methodology for Dynamic Data-Driven Online Flight Capability Estimation |
| title_short | Methodology for Dynamic Data-Driven Online Flight Capability Estimation |
| title_sort | methodology for dynamic data driven online flight capability estimation |
| url | http://hdl.handle.net/1721.1/106347 https://orcid.org/0000-0003-2156-9338 |
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