Identification of a quasi-LPV model for wing-flutter analysis using machine-learning techniques

• Type: Chapter or Part of a Book
• Date: 2019

Authors

Romano, RA (Author) Other; Lima, MML (Author) Other; P. Lopes dos Santos (Author) FEUP; Perdicoúlis, TPA (Author) Other

Book

Title: Data-Driven Modeling, Filtering and Control

Pages: 37-55

ISBN: 9781785617126

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00V-MTC

DOI: 10.1049/pbce123e_ch3

Abstract (EN): Aerospace structures are often submitted to air-load tests to check possible unstable structural modes that lead to failure. These tests induce structural oscillations stimulating the system with different wind velocities, known as flutter test.An alternative is assessing critical operating regimes through simulations. Although cheaper, modelbased flutter tests rely on an accurate simulation model of the structure under investigation. This chapter addresses the data-driven flutter modeling using state-space linear parameter varying (LPV) models. The estimation algorithm employs support vector machines to represent the functional dependence between the model coefficients and the scheduling signal, which values can be used to account for different operating conditions. Besides versatile, that model structure allows the formalization of the estimation task as a linear least-squares problem. The proposed method also exploits the ensemble concept, which consists of estimating multiple models from different data partitions. These models are merged into a final one, according to their ability to reproduce a validation data segment.A case study based on real data shows that this approach resulted in a more accurate model for the available data. The local stability of the identified LPV model is also investigated to provide insights about critical operating ranges as a function of the magnitude of the input and output signals. © The Institution of Engineering and Technology 2019.

Language: English

Type (Professor's evaluation): Scientific