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.

Chapter Contents:

• 3.1 Introduction
• 3.2 LPV state-space model parameterization
• 3.3 Model estimation
• 3.3.1 Parameter reconstruction
• 3.4 Ensemble estimation approach
• 3.5 Wing-flutter model identification
• 3.6 Concluding remarks
• References

Inspec keywords: aerodynamics; nonlinear control systems; control system synthesis; state-space methods; support vector machines; least squares approximations; aerospace components; linear systems; learning (artificial intelligence); identification

Other keywords: linear least-squares problem; aerospace structures; functional dependence; validation data segment; model structure; machine-learning techniques; unstable structural modes; wind velocities; state-space linear parameter; quasiLPV model; estimation task; flutter test; scheduling signal; estimation algorithm; critical operating regimes; air-load tests; wing-flutter analysis; identified LPV model; model-based flutter tests; structural oscillations; support vector machines; operating conditions; model coefficients; data-driven flutter modeling; multiple models

Subjects: Simulation, modelling and identification; Control system analysis and synthesis methods; Linear control systems; Nonlinear control systems; Other topics in statistics; Aerospace control; Interpolation and function approximation (numerical analysis); Knowledge engineering techniques