Assessing Sensor-Model-Fusion Technologies on a Flexible Aeroelastic Wing Demonstrator through Wind Tunnel Testing in the DLR Project SAFER²

Abstract

This study discusses the experimental exploration of novel sensor-model-fusion technologies on a flexible aeroelastic wing demonstrator through wind tunnel testing, as part of the DLR project SAFER2. The aeroelastic wing demonstrator is excited via inflow gusts and disturbances, generated by a gust rig, or using control surface deflections. A large variety of sensor systems are employed to capture these excitations and measure the loads, thereby enabling the effective testing of sensor-model-fusion technologies. Among the latter, active control methods to alleviate gust loads and to augment the wing damping, as well as an online-monitoring framework to identify structural modal parameters are explored. Two gust load alleviation strategies are investigated, which are either based on a robust control approach or leverage reinforcement learning techniques. The onlinemonitoring framework implements various operational modal analysis methods, coupling them with a linear Kalman filter. The damping augmentation controller uses a destabilization/stabilization approach, featuring a novel input-output blending method as well as a saturation mechanism to guarantee a safe operation. All sensor-model-fusion technologies tested yield promising results, whether these concern the effective reduction of gust-induced loads (e.g., wing-root bending moment), the accurate online monitoring of modal parameters, or the controlled destabilization and re-stabilization of the wing model using damping augmentation.