Data-driven reduced order control for partially observed fluid systems

Palash Sashittal, Daniel J. Bodony

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a novel method for non-intrusive reduced order control of partially observed flow systems. We formulate a rank-constrained matrix optimization problem for the maximum likelihood estimation of the reduced order model. An adjoint-based method is used for the gradient extraction and Riemannian optimization is performed for efficient convergence to the optimal solution. The resulting reduced order model is then used to design a Linear-Quadratic-Gaussian (LQG) controller. We demonstrate the performance of the proposed reduced order control method on the flow past an inclined flat plate at a high angle of attack and successfully prevent vortex shedding in the wake of the flat plate.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2020 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105951
DOIs
StatePublished - 2020
EventAIAA Scitech Forum, 2020 - Orlando, United States
Duration: Jan 6 2020Jan 10 2020

Publication series

NameAIAA Scitech 2020 Forum
Volume1 PartF

Conference

ConferenceAIAA Scitech Forum, 2020
CountryUnited States
CityOrlando
Period1/6/201/10/20

ASJC Scopus subject areas

  • Aerospace Engineering

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