Learning-based Adaptive Gust Mitigation with Oscillating Wings

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Scitech2023_GustMitigation.pdf (1.12 MB)

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https://arc.aiaa.org/doi/10.2514/6.2023-0275

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https://doi.org/10.2514/6.2023-0275
 http://hdl.handle.net/11603/26946

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UMBC Mechanical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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Author/Creator ORCID

https://orcid.org/0000-0002-8543-6019
 https://orcid.org/0000-0001-9326-0319
 https://orcid.org/0000-0003-3071-0487
 https://orcid.org/0000-0002-4146-6275

Date

2023-01-19

Type of Work

conference papers and proceedings
postprints
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Citation of Original Publication

Poudel, Naresh, et al. "Learning-based Adaptive Gust Mitigation with Oscillating Wings" AIAA SCITECH 2023 Forum (23-27 January 2023). https://doi.org/10.2514/6.2023-0275.

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UMBC High Performance Computing Facility (HPCF)

Abstract

This paper investigates the application of a learning-based adaptive controller to mitigate the effect of gust on the lift generated by an airfoil in an unsteady flow environment. A high-order accurate CFD model is used to model the unsteady flow over a pitching airfoil. Open-loop simulations of the CFD model are used to ascertain feasible lift commands. The learning-based adaptive controller is based on the retrospective cost adaptive control (RCAC). First, RCAC is used to regulate the lift coefficient of the airfoil in a nominal case without gust. Next, the effect of the hyperparameters of the adaptive control on the closed-loop performance is investigated. Finally, we used RCAC to regulate the lift coefficient and mitigated the effect of gust on the airfoil.