Adaptive State Estimation and Real-Time tracking of Aeroelastic Wings with Augmented Kalman filter and Kernelized Correlation Filter

Abstract

Advancements in aircraft controllers and the tendency towards increasingly lighter and more flexible aircraft designs create the need for adaptive and intelligent control systems. While lighter aircraft structures have the potential to show better structural and aerodynamic efficiency, they are also more susceptible to dynamic loads. A key aspect to account for the flexibility of the structure in a closed-loop design is aeroelastic state estimation and the feedback of wing motion (elastic states). A potential non-invasive approach to provide this measurement for control-feedback is visual tracking, with fuselage-mounted cameras observing the motion of the wing. In particular, high-speed visual tracking with correlation filters such as KCF (Kernelized Correlation Filter), allow to efficiently and robustly correlate between two samples with kernelized linear regression. A purely visual tracking filter however does not contain information regarding the dynamics of the system subject to tracking and may fail under marker loss and occlusion. To increase the robustness of the racking an EKF (extended Kalman filter) is added to the tracking filter acting as a KCF-EKF tracking couple. The Kalman filter is further augmented into augmented Kalman filter form, to allow joint on-line estimation of the model states and parameters. This proposed tracking approach is used to adaptively reconstruct the motion of a very flexible wing in real-time subject to gust excitation in the OJF (Open Jet Facility) wind tunnel at the Technical University of Delft. The method shows a good agreement with time and frequency domain analysis of the reference data measured by a laser vibrometer and demonstrated the effectiveness of KCF-AEKF couple under the presence of marker loss and model uncertainties for a model-free control approach.