ORCID Profile
0000-0002-7143-0441
Current Organisation
University of Adelaide
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Publisher: IEEE
Date: 07-2015
Publisher: Informa UK Limited
Date: 11-08-2021
Publisher: IEEE
Date: 07-2019
Publisher: SAGE Publications
Date: 28-05-2021
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.
Publisher: Elsevier BV
Date: 03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 02-2019
Publisher: SAGE Publications
Date: 15-01-2018
Abstract: This paper proposes a novel adaptive nonlinear controller based on neural-networks (NNs) for active suppression of airfoil flutter (ASAF) from the optimal control perspective. Optimal control laws for locally nonlinear systems are synthesized in real time by solving the Hamilton–Jacobi–Bellman equation online with a proposed new form of NN-based value function approximation (VFA) and an extended Kalman filter. A systematic procedure based on linear matrix inequalities is further proposed for designing a scheduled parameter matrix that generalizes the new form of VFA to globally nonlinear systems to suit ASAF applications. Un-modeled dynamics are captured using an NN identifier. Comparisons drawn with a linear-parameter-varying optimal controller in wind-tunnel experiments confirm the effectiveness and validity of the proposed control scheme.
No related grants have been discovered for Difan Tang.