Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/114662
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Type: Conference paper
Title: Nonlinear optimal control for active suppression of airfoil flutter via a novel neural-network-based controller
Author: Tang, D.
Chen, L.
Tian, Z.
Hu, E.
Citation: Proceedings of the 2017 25th Mediterranean Conference on Control and Automation (MED), 2017 / pp.253-258
Publisher: IEEE
Issue Date: 2017
Series/Report no.: Mediterranean Conference on Control and Automation
ISBN: 9781509045334
ISSN: 2325-369X
2473-3504
Conference Name: 2017 25th Mediterranean Conference on Control and Automation (MED 2017) (03 Jul 2017 - 06 Sep 2017 : Valletta, Malta)
Statement of
Responsibility: 
Difan Tang, Lei Chen, Zhao F. Tian and Eric Hu
Abstract: This paper proposes a novel nonlinear controller based on neural networks (NNs) for active suppression of airfoil flutter (ASAF). Aeroelastic flutter can damage airfoils if not properly controlled. Existing optimal controllers for ASAF are sensitive to modeling errors while other controllers less prone to uncertainties do not provide optimal control. This study, thus, focuses on solving these problems by deriving a new intelligent model-based control scheme capable of synthesizing nonlinear near-optimal control laws in real time according to a known model and online updated system dynamics. A four-degreesof- freedom aeroelastic model that has nonlinear translational and torsional stiffness and employs leading/trailing-edge control surfaces as control inputs is considered. Optimal control laws for the nonlinear aeroelastic system is synthesized by solving the Hamiltonian-Jacobi-Bellman equation through NN-based value function approximation (VFA) and synchronous policy iteration in a Critic-Actor configuration. A systematic approach based on linear matrix inequalities (LMIs) is proposed for the design of a scheduled parameter matrix for the VFA. An NN-based identifier is also derived to capture un-modeled dynamics online. Extended Kalman filters are employed to tune NN parameters. The proposed controller was tested in wind-tunnel experiments. Comparisons drawn with a linearparameter- varying optimal controller confirms the effectiveness and validity of the proposed control scheme.
Keywords: Artificial neural networks; automotive components; optimal control
Rights: © 2017 IEEE
RMID: 0030075399
DOI: 10.1109/MED.2017.7984127
Appears in Collections:Mechanical Engineering conference papers

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