Flutter Suppression of Wind Turbine Blade Based on RBF Neural Network Compensation Backstepping Control

Abstract Aiming at the critical flutter problem of wind turbine blades, which is between classical and stall flutter, a suppression scheme based on radial basis function (RBF) neural network friction compensation backstepping presented. The structure model typical 2D section bending twist spring-mass-damper, rotor variable exciter second-order with disturbance incorporated to control blade. A modified quasi - steady state aerodynamic was used for aerodynamics actuation. RBF block-controlled controller designed stability theorem Lyapunov function, approximates frictional interference nonlinear characteristics through network, cancels existing in actuator rotor. Four speed environments were selected analyze response blades under different speeds, effects two speeds verify ability approach function. results show that can improve robustness suppress blades.