Theoretical modeling and the sliding mode control (SMC) of an active trailing-edge flap of a wind turbine blade based on the adaptive reaching law are investigated. The blade is a single-celled thin-walled composite structure using circumferentially asymmetric stiffness (CAS) design, exhibiting displacements of flap-wise/twist coupling. A reduced structural model originated from the variation method is used to model the structure of the blade, the structural damping of which is computed. The trailing-edge flap is a rigid structure that is embedded in and hinged to the blade host structure, and it is driven by two pairs of pneumatic cylinders moving in reverse. Flutter suppression for the large-amplitude vibration of the blade tip is investigated based on an active trailing-edge flap structure and SMC algorithm using the adaptive reaching law. The controlled responses of flap-wise/twist displacements and control inputs (the angles of the trailing-edge flap) are illustrated, with obvious simulation effects demonstrated. An experimental platform for driving the pneumatic cylinders verifies the effectiveness of the control algorithm using the adaptive reaching law and the effectiveness of the selected pneumatic transmission scheme controlled by another adaptive SMC based on the minimum parameter learning of neural networks.

Theoretical modeling and vibration control for divergent motion of thin-walled pre-twisted wind turbine blade have been investigated based on “linear quadratic Gaussian (LQG) controller using loop transfer recovery (LTR) at plant input” (LLI). The blade section is a single-celled composite structure with symmetric layup configuration of circumferentially uniform stiffness (CUS), exhibiting displacements of vertical/lateral bending coupling. Flutter suppression for divergent instability is investigated, with blade driven by nonlinear aerodynamic forces. Theoretical modeling of CUS-based structure is implemented based on Hamilton variational principle of elasticity theory. The discretization of aeroelastic equations is solved by Galerkin method, with blade tip responses demonstrated. The LLI controller is characterized by LTR at the plant input. The effects of LLI controller are achieved and illustrated by displacement responses, controller responses and frequency spectrum analysis, respectively.

Vibration and control of cantilever blade with bending-twist coupling (BTC) based on trailing-edge flap (TEF) by restricted control input are investigated. The blade is a thin-walled structure using circumferentially asymmetric stiffness (CAS) configuration, with TEF embedded and hinged into the host composite structure along the entire blade span. The TEF structure is driven by quasi-steady aerodynamic forces. Vibration control is investigated based on linear matrix inequation (LMI) algorithm using restricted control input (LMI/RCI). Flutter suppression of BTC displacements and the angle of TEF (i.e. the practical control input) are illustrated, with apparently controlled effects demonstrated. The restricted control input signals are used to driven the TEF to explore the scope of the feasibility of the practical TEF angle, which is displayed by a virtual simulation platform. The platform verifies the feasibility of the hardware implementation for the control algorithms.

In this study, vibration control, a behavior which subordinates to stall-induced nonlinear vibration and amplitude control of a wind turbine’s blade section, based on unified pitch motion driven by slider-linkage mechanism, is investigated by using an iterative learning control (ILC) method. The nonlinear dynamical system is a nonlinear aeroelastic system. The aeroelastic system equations consist of three parts: the nonlinear structural equations derived by using Lagrange’s equations, the improved stall-induced nonlinear ONERA (ISNO) aerodynamic equations, and the pitch control equation. The ISNO model is not only suitable for the actual external pitch motion, but also suitable for the solution by using an ILC algorithm due to its fitted nonlinear aerodynamic coefficients. The ILC algorithm used here is an improved iterative learning algorithm (IILC) which considers the large-range, linearized, residual terms, and realizes gain adaptive tuning based on PID controller. On the one hand, it can control the amplitude of an unsteady flutter through trajectory tracking. On the other hand, when the preset value of the amplitude of the ideal trajectory is very small, it can make the system directly tend to convergence and stability of a nonlinear aeroelastic system. To simplify the extremely difficult iterative process, the pitch movement can track the elastic twist displacement in time, thus simplifying the aeroelastic equations and accelerating the IILC iteration process. Therefore, amplitude control for flap-wise/lead-lag displacements is realized by the unified pitch motion and the trajectory tracking controlled by using the IILC algorithm.

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