Vibration control and trajectory tracking for nonlinear aeroelastic system based on adaptive iterative learning control. (July 2022)

• Record Type: Journal Article
• Authors: Liu, Tingrui
• Is Part Of: Noise & vibration worldwide. Volume 53:Number 7/8(2022)
• Page Start: 390
• Page End: 403
• Publication Date: 2022-07
• Subjects: Iterative learning control -- nonlinear aeroelasticity -- proportional-derivative control -- pitch control -- refined OPC Technology -- single neuron -- vibration control; Noise control -- Periodicals; Damping (Mechanics) -- Periodicals; Soundproofing -- Periodicals; Damping (Mechanics); Noise control; Soundproofing; Periodicals; 620.205
• DOI: 10.1177/09574565221114659
• Languages: English
• ISSNs: 0957-4565

Abstract

Vibration control and trajectory tracking for nonlinear aeroelastic system of 2D airfoil of wind turbine subjected to flap-wise vibration and independent pitch motion, are investigated based on hydraulic pitch control and adaptive iterative learning (AIL) control. The aeroelastic system is simplified by an external slubber, the host material of which is a composite material with shape memory alloy (SMA) wires embedded in. The independent pitch motion is driven by two-way-rack cylinder and hydraulic servo valve. The control of divergent unstable motions is implemented by tracking the vibrations of the preset displacements. The control law of AIL algorithm combines iterative algorithm and proportional-derivative (PD) control. The control parameters of PD controller are adjusted by proportional-derivative control based on single neuron (PDC/SN) algorithm. Based on the convergence analysis of the iterative algorithm, the tracking results (including position tracking and speed tracking) of the iterative process are shown, and the convergence of the absolute values of the relative errors during tracking is verified. The experiment based on process control and a refined OPC Technology verifies the feasibility of the engineering application of the proposed algorithm in nonlinear aeroelasticity. The consistency between the real-time dynamic tracking process shown on the experimental panel and the theoretical tracking process illustrated by simulation verifies the effectiveness of the control algorithm for engineering application. The research implication for the design of current turbine manufacturers is that it provides a design idea of blade buffer based on smart materials, and an implementation idea for the engineering application of adaptive AIL algorithm.