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Abstract-A novel actuator controller is proposed in this paper for active-truss-based morphing wings (ATBMWs). An ATBMW is a new type of smart structure capable of smooth and continuous profile change, and has the potential to provide better stealth and aerodynamic performance over airfoils with discrete control surfaces. However, the sophisticated ATBMW framework and large amount of highly interacted actuators make it difficult to obtain the overall rigid-body dynamics of the wing for controller design and inconvenient to tune controllers on board. The focus of this study is thus to solve the aforementioned problems by developing an actuator-level control scheme that does not rely on the wing rigid-body dynamics and on-board tuning. The linear-quadratic-Gaussian (LQG) controller is adopted for actuator trajectory tracking, and a novel unknown-input estimator (UIE) is devised to handle un-modeled dynamics. By integrating the UIE with the LQG algorithm, a new tracking controller with enhanced tolerance to uncertainties is constructed. It is shown in simulations and experiments on an ATBMW prototype that the proposed UIE-integrated LQG controller can be designed simply using the known actuator dynamics without on-board tuning, and superior trajectory tracking of actuators was observed despite the presence of un-modeled dynamics and exogenous disturbances.
A new policy-iteration algorithm using neural networks (NNs) is proposed in this paper to synthesize optimal control laws online for continuous-time nonlinear systems. Latest advances in this field realize synchronous policy iteration but meanwhile require an additional tuning loop or a logic switch mechanism to maintain system closed-loop stability. A new algorithm is thus derived in this paper to address this limitation. The optimal control law is found by solving the Hamilton-Jacobi-Bellman (HJB) equation for the associated value function via synchronous policy iteration in a critic-actor configuration. As a major contribution, a new form of NN approximation for the value function is proposed, offering the closed-loop system asymptotic stability without additional tuning scheme or logic switch mechanism. As a second contribution, an extended Kalman filter (EKF) is introduced to estimate the critic NN parameters for fast convergence. The efficacy of the new algorithm is verified by simulations.
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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.
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