A neural network approach for improving airfoil active flutter suppression under control-input constraints
Date
2021
Authors
Tang, D.
Chen, L.
Tian, Z.F.
Hu, E.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Journal of Vibration and Control, 2021; 27(3-4):451-467
Statement of Responsibility
Difan Tang, Lei Chen, Zhao F Tian and Eric Hu
Conference Name
Abstract
This 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.
School/Discipline
Dissertation Note
Provenance
Description
Article first published online: May 28, 2020
Access Status
Rights
© The Author(s) 2020.