Active Flutter Mitigation Testing on the FLEXOP Demonstrator Aircraft

Abstract

The paper details the research and corresponding implementation and testing steps of the FLEXOP demonstrator aircraft. Within the EU funded project an unmanned demonstrator aircraft is built to validate the mathematical modelling, flight control design and implementation side of active flutter mitigation. In order to validate the different methods and tools developed in this project, a flight test campaign is planned, in which the design and manufacturing of stiff wings (-0), are compared with very flexible wings (-1) with active flutter control, to see the overall benefit vs. risk of such technology. The mathematical models of the aircraft are first developed using FEM and CFD tools, what are later reduced by model order reduction techniques. The high-fidelity models are updated using Ground Vibration Test results. Manufacturing tolerances and variations in aircraft parameters are captured by systematic modelling of parametric and dynamic uncertainties. Both the simulation environment and the control design framework use different modelling fidelity, what are described within the paper. Reduced models are developed using two distinctive methods, respecting the control design needs: top-down balanced LPV reduction and bottom-up structure preserving methods. Based on the reduced order models various control design techniques have been elaborated by the consortium partners. In particular DLR developed and implemented a modal control method using H2 optimal blends for inputs and outputs. University of Bristol developed structured H-infinity optimal control methods, while SZTAKI proposed a worst-case gain optimal method structured controller synthesis method handling parametric and complex uncertainties. After the brief introduction of hardware-in-the-loop test setup and the description of mission scenarios the implementation issues of the baseline and flutter controllers are discussed. DLR and SZTAKI flutter controllers are evaluated in a hybrid software-/ hardware-in-the-loop test setup as at this stage of development the latter can not tolerate the estimated delay of the hardware system but their comparison is advantageous before future developments. Recommendations on active flutter mitigation methods are given based on the experience of synthesis and implementation of these controllers. Flight test results will follow these experiments, once the flight testing of the flutter wing commences.

Ground Testing of the FLEXOP Demonstrator Aircraft

“…The updated FEM model shows a very good agreement with the measured displacements. More details about the model updating approach are given by Takarics et al 17 The same updating procedure was also successfully applied to wing (-2).…”

“…SIL-HIL results are presented in another work also because of space constraints of the current work see. 21…”

“…While this paper is focused on the ground testing campaign performed within the FLEXOP project it has been presented together with papers on flight testing of aeroelastically tailored wings 16 and active flutter mitigation testing performed on the FLEXOP aircraft. 17 The structure of the paper reflects all the three major test groups. Section II introduces the static and the GVT test, followed by the presentation of the strain-based wing shape and load reconstruction and the airworthiness test.…”

Identification and Modeling of the Airbrake of an Experimental Unmanned Aircraft

“…An illustrative three view of the actual demonstrator is depicted in Fig. 2 together with main technical data in Table 1 (regarding the flutter speed see [17]). The high aspect ratio wings feature a moderate leading edge sweep to account for the characteristic bendtwist-coupling of high-subsonic planform designs.…”

Construction of an Uncertainty to Maximize the Gain at Multiple Frequencies

“…E.g. it can be incorporated in a higher fidelity nonlinear simulation, especially for systems with multiple resonance frequencies such as hard disk drives [12], and flexible aircraft [13]. In addition, it can be useful as part of control design methods that require bad samples of dynamic uncertainty [2].…”