Design and Real-Time Implementation of a Vision-Based Adaptive Model-Free Morphing Wing Motion Control Method [PPT]

Design and Real-Time Implementation of a Vision-Based Adaptive Model-Free Morphing Wing Motion Control Method [PPT]

Mkhoyan, T. (TU Delft Aerospace Structures & Computational Mechanics)
Wang, Xuerui (TU Delft Aerospace Structures & Computational Mechanics)
de Visser, C.C. (TU Delft Control & Simulation)
De Breuker, R. (TU Delft Aerospace Structures & Computational Mechanics)

2020

The advancements made in aircraft control methodology and the tendency towards increasingly lighter aircraft structures open the opportunity to higher structural performance and aerodynamic efficiency. However, with the reduction of structure weight, the structure stiffness reduces typically, which makes the structure more susceptible to external dynamic loads. How the flexibility affects the dynamics of the system, in particular in closed-loop control, cannot always be determined in the early stage of the design process. This introduces uncertainties to the dynamic model and consequently leads to inaccurate performance evaluation. Our proposed approach to fully utilize the potential of the lighter aircraft structure is to actively morph it using distributed actuators commanded by a real-time multi-objective controller. In the literature, model-based feedback control methods are widely used for flexible structure motion suppression. However, the performance of model-based controllers is impaired by model uncertainties and external disturbances. Another challenge in flexible structure control is the real-time state estimation. Although accelerometers and strain gauges can be used to capture the structural vibrations, these sensors have to be installed within the structure, which increases the difficulties in maintenance. A potentially universal, model-free, and non-invasive approach is visual tracking. In combination with robust model-free control laws, this has the potential to create smart adaptive structures that are capable of vibration suppression. In this study, an adaptive model-free state estimation methodology based on visual feedback is developed and demonstrated in unison with a non-linear model-free robust control method in a closed-loop system. The experimental setup consists of high-speed GIGE cameras observing oscillations from a clamped beam subject to disturbances at 140 Hz. The task of the controller is to reject the disturbances through a shaker input under the presence of parametric model uncertainties and external disturbances. The visual tracking utilizes adaptive estimation utilizing high-speed KCF (Kernelized Correlation Filter) tracking and an AEKF (Augmented Extended Kalman Filter) with augmented time-varying mass, stiffness, and damping states. The inclusion of the augmented Kalman filter adds robustness to occlusion and model uncertainties. The nonlinear model-free control method is developed in the incremental control framework, hybridized with sliding mode control for robustness enhancement. The control effectiveness matrix used by the controller is adapted online. Furthermore, the state and state derivative feedback signals are provided by the visual system in real-time. This research is a part of the Smart-X wing project, which represents an autonomous smart morphing wing that is capable of in-flight performance optimisation of multiple objectives. It is shown in this research that the combination of adaptive visual tracking and robust control shows how a flexible uncertain structure can be transformed into a controlled adaptive smart structure. This combination of visual tracking and control shows great potential for robust and model-free stabilization and vibration suppression. Further uses of the methodology are discussed for use in tracking problems for flexible and aeroelastic structures.

http://resolver.tudelft.nl/uuid:0155bc24-cac4-4846-93d0-a509bd2b8a47

ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2020, 2020-09-15, Virtual/online event due to COVID-19

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© 2020 T. Mkhoyan, Xuerui Wang, C.C. de Visser, R. De Breuker