Introduction F OR lightweight and flexible structures, it is important to measure and suppress the flow-induced vibrations caused by interactions between fluid and structures. Dynamic aeroelastic instabilities, such as flutter, involve the interaction of aerodynamic, inertia, and elastic forces of flight structures. Because flutter may cause disastrous structural failures in flight, the prediction of stability boundary and the suppression of flutter are very important analysis issues for flight structures. In recent years, several active control strategies have been studied to modify favorably the behavior of aeroelastic systems using smart material and structure technologies. Lazarus et al.1 successfully applied multi-input/multi-output controls to suppress vibration and flutter of a platelike lifting surface with surface-bonded piezoelectric actuators. Han et al.2 performed numerical and experimental investigation on active flutter suppression of a swept-back cantilevered plate. Application of piezoelectric actuation to flutter control of a more realistic wing model was achieved under the Piezoelectric Aeroelastic Response Tailoring Investigation program at NASA Langley Research Center.3 Recently, fiber bragg grating (FBG) sensors have been increasingly studied for a variety of applications: health monitoring, vibration measurement, nondestructive testing and so on. Gratings are simple, intrinsic sensing elements that can be photoinscribed into a silica fiber and have the same advantages normally attributed to fiber sensors. In addition, the devices have an inherent self-referencing capability and are easily multiplexed in a serial fashion along a single fiber.4 An overview of FBG sensors and systems is presented in Ref. 4. This paper investigates dynamic application of an FBG sensor system to the flutter suppression of a composite plate structure. In practical situations, the modeling of an aeroelastic system is complicated and the dynamic characteristics of an aeroelastic system changes with respect to the airflow speed. Therefore, the adaptiveness and the robustness are key features for an aeroelastic control system. A neuroadaptive feedback control algorithm is used in this study. The control system consists of the neuroidentification model and the neurocontroller. The real-time implementation of the adaptive controller is performed using a digital signal processing (DSP) board. The effectiveness of the flutter suppression system is evaluated via wind-tunnel testing.
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