![]() An optimisation search method is applied to determine the best locations for measurement and excitation by introducing Fisher’s information matrix. The full system model includes the structural dynamic model, electromechanical coupling model and fast aerodynamic computation model. A new parameterised modelling method at the full system level based on the generalised force equivalence for DWT flutter systems is proposed herein. They can be used to test the aeroelastic and aeroservoelastic stability of smart aircraft or high-speed flight vehicles. However, the LPV anti-windup compensator not only enhances the nominal controller’s performance but also helps the nominal controller to stabilize the unstable aeroelastic system when encountering serious actuator saturation.ĭry wind-tunnel (DWT) flutter test systems model the unsteady distributed aerodynamic force using various electromagnetic exciters. ![]() Although the nominal LPV controller may have superior performance in linear simulation in which the saturation effect is ignored, the results of the numerical simulations show that the nominal LPV controller fails to suppress the Body Freedom Flutter (BFF) when encountering the actuator saturation. ![]() Secondly, based on the control-oriented LPV model, an AFS controller in LPV framework which is composed of a nominal LPV controller and an LPV anti-windup compensator is designed to suppress the aeroelastic vibration and overcome the performance degradation caused by actuator saturation. ![]() And then, the unstable aeroelastic dynamics beyond critical airspeed are ‘predicted’ by extrapolating the resulting LPV model. Firstly, with the aid of LPV model order reduction method and State-space Model Interpolation of Local Estimates (SMILE) technique, a set of high-fidelity Linear Time-Invariant (LTI) models which are usually derived from flight tests at different subcritical airspeeds are reduced and interpolated to construct an LPV model of an aeroelastic system. To tackle these two problems, a new active controller design procedure is proposed to suppress flutter in this paper. On the other hand, saturation of the actuator may degrade the closed-loop performance, which was often neglected in the past work. On the one hand, due to the fatal risk of flight test near critical airspeed, it is hard to obtain the accurate mathematical model of the aeroelastic system from the testing data. Nevertheless, the flutter suppression technique is facing two severe challenges. In recent years, the Active Flutter Suppression (AFS) employing Linear Parameter-Varying (LPV) framework has become a hot spot in the research field. Finally, the performance of the proposed procedure is validated by real flight flutter test data. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. In the second step, with prior knowledge of physical poles, the improved frequency-domain maximum likelihood (ML) estimator is presented for building accurate reduced-order model. After parameter identification, the dominant physical modes are determined by clear stabilization diagrams and clustering technique. The well-known poly-reference least squares complex frequency (p-LSCF) algorithm is firstly employed for modal parameter identification from frequency response measurement. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant (LTI) models by using system identification from flutter testing data. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Recently, flutter active control using linear parameter varying (LPV) framework has attracted a lot of attention.
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