A neural network approach for improving airfoil active flutter suppression under control-input constraints

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dc.contributor.authorTang, D.
dc.contributor.authorChen, L.
dc.contributor.authorTian, Z.F.
dc.contributor.authorHu, E.
dc.date.issued2021
dc.descriptionArticle first published online: May 28, 2020
dc.description.abstractThis study deals with improving airfoil active flutter suppression under control-input constraints from the optimal control perspective by proposing a novel optimal neural-network control. The proposed approach uses a modified value function approximation dynamically tuned by an extended Kalman filter to solve the Hamilton–Jacobi–Bellman equality online for continuously improved optimal control to address optimality in parameter-varying nonlinear systems. Control-input constraints are integrated into the controller synthesis by introducing a generalized nonquadratic cost function for control inputs. The feasibility of using a performance index involving the nonquadratic control-input cost with the modified value function approximation is examined through the Lyapunov stability analysis. Wind tunnel experiments were conducted for controller validation, where an optimal controller synthesized offline via linear parameter-varying technique was used as a benchmark and compared. It is shown, both theoretically and experimentally, that the proposed method can effectively improve airfoil active flutter suppression under control-input constraints.
dc.description.statementofresponsibilityDifan Tang, Lei Chen, Zhao F Tian and Eric Hu
dc.identifier.citationJournal of Vibration and Control, 2021; 27(3-4):451-467
dc.identifier.doi10.1177/1077546320929153
dc.identifier.issn1077-5463
dc.identifier.issn1741-2986
dc.identifier.orcidTang, D. [0000-0002-7143-0441]
dc.identifier.orcidChen, L. [0000-0002-2269-2912]
dc.identifier.orcidTian, Z.F. [0000-0001-9847-6004]
dc.identifier.orcidHu, E. [0000-0002-7390-0961]
dc.identifier.urihttp://hdl.handle.net/2440/126687
dc.language.isoen
dc.publisherSAGE Publications
dc.rights© The Author(s) 2020.
dc.source.urihttps://journals.sagepub.com/home/jvc
dc.subjectActive control; airfoil flutter; constrained inputs; nonlinear control; neural networks; optimal control
dc.titleA neural network approach for improving airfoil active flutter suppression under control-input constraints
dc.typeJournal article
pubs.publication-statusPublished

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