Aeroelasticity & Structural Dynamics in a Fast Changing World
17 – 21 June 2024, The Hague, The Netherlands
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Modelling complex actuator and sensor architectures for gust alleviation systems of flexible aircraft


Go-down ifasd2024 Tracking Number 29

Presentation:
Session: Gust 3
Room: Room 1.4/1.5
Session start: 13:30 Wed 19 Jun 2024

Stefanie Düssler   s.dussler20@imperial.ac.uk
Affifliation: Imperial College London

Thulasi Mylvaganam   t.mylvaganam@imperial.ac.uk
Affifliation: Imperial College London

Rafael Palacios   r.palacios@imperial.ac.uk
Affifliation: Imperial College London


Topics: - Computational Aeroelasticity (High and low fidelity (un)coupled analysis methods:), - Dynamic Loads (High and low fidelity (un)coupled analysis methods:), - Aeroservoelasticity (Vehicle analysis/design using model-based and data driven models), - Active Control and Adaptive Structures (Vehicle analysis/design using model-based and data driven models)

Abstract:

We propose a modular framework for designing and testing control systems for flexible air vehicles that may exhibit geometric nonlinearities and rigid/elastic couplings. This framework supports various controllers and levels of modeling fidelity. It can accurately model nonlinear aeroelastic effects and function as a model-in-the-loop using a network interface. We demonstrate its capabilities using an aircraft model with large wing deformations. A linear quadratic Gaussian controller is developed based on a linear reduced-order model derived from the aircraft’s cruise flight nonlinear equilibrium and designed for gust alleviation. In the design process, we observe substantial rigid/elastic coupling effects in the aircraft, which markedly impact the control design process. We also identify suitable actuator and sensor architectures with the sensor choice being depending on the nonlinear aeroelastic model characteristics. This controller is initially validated using a linear full-order model and then tested in a nonlinear simulation environment. While the controller fulfills the load alleviation and stabilization requirements, a reduced controller performance is observed with the higher-fidelity model in the loop necessitating a thorough reevaluation and adjustment of the control strategy in this computationally demanding setting.