Sliding Mode Control of Active Trailing-Edge Flap Based on Adaptive Reaching Law and Minimum Parameter Learning of Neural Networks

Tingrui Liu, Ailing Gong, Changle Song and Yuehua Wang
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Tingrui Liu: College of Mechanical & Electronic Engineering, Shandong University of Science & Technology, Qingdao 266590, China
Ailing Gong: Business School, Qingdao University of Technology, Qingdao 266520, China
Changle Song: College of Mechanical & Electronic Engineering, Shandong University of Science & Technology, Qingdao 266590, China
Yuehua Wang: School of Mechanical and Vehicle Engineering, Linyi University, Linyi 276000, China

Energies, 2020, vol. 13, issue 5, 1-21

Abstract: 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.

Keywords: trailing-edge flap; circumferentially asymmetric stiffness; sliding mode control; adaptive reaching law; minimum parameter learning of neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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