The developments in the field of aerospace materials and structures allow the more light weight air vehicles. However, the aircraft body, particularly wing, can deform more appreciably due to the occurrence of flow separation and flutter. Therefore, active control is necessary in order to maintain structural integrity. One of the proposed control methods uses visual tracking for structural state estimation, which reduces complexity in terms of hardware and data processing requirements compared to the conventional method using inertial measurement units and gyroscopes. However, the wing displacement measurement involves a high number of states to estimate. An idea is to implement a model reduction method to be implemented as a mathematical model of the aeroelastic wing to quicken the state estimation process. The proposed method of model reduction by using Modified Frequency-Limited Model Reduction (MFLMR) method by Gugercin and Antoulas (2004) is then augmented with the application of singular perturbation step and validated with simulation in stochastic Gaussian gust regimes for two wing models. The effect of additional singular perturbation step is presented. The results show that the proposed MFLMR method with singular perturbation prevails to replicate the true values in the simulated condition with different wing models in both time domain and frequency domain with smaller error autocorrelation. Further analysis is recommended to be focused on the implementation of the proposed model reduction method to the state and parameter estimation in order to maintain the high sample rate that can be attained by using the controller scheme with visual tracking.@en

In this study, the design and development of an autonomous morphing wing concept were investigated. The morphing wing was developed in the scope of the Smart-X project, aiming to demonstrate in-flight performance optimisation. This study proposed a novel distributed morphing concept, with six Translation Induced Camber (TRIC) morphing trailing edge modules, interconnected with triangular skin segments joined by an elastomer material to allow seamless variation of local lift distribution along the wingspan. A FSI structural analysis tool was developed, to achieve a feasible design, capable of reaching target shapes and minimising the actuation loads with fibreglass weave material. A conventional actuator and kinematic mechanism were selected such that both static and dynamic requirements in terms of bandwidth, actuation force and stroke were fulfilled. The integration of smart sensing technologies and active morphing design developed for the Smart-X wing is facilitated in static and dynamic wind-tunnel tests at the Open Jet Facility (OJF) at the Delft University of technology, with multi-objective control of the active morphing system. @en

This paper deals with the simultaneous gust and maneuver load alleviation problem of a seamless active morphing wing. The incremental nonlinear dynamic inversion with quadratic programming control allocation and virtual shape functions (denoted as INDI-QP-V) is proposed to fulfill this goal. The designed control allocator provides an optimal solution while satisfying actuator position constraints, rate constraints, and relative position constraints. Virtual shape functions ensure the smoothness of the morphing wing at every moment. In the presence of model uncertainties, external disturbances, and control allocation errors, the closed-loop stability is guaranteed in the Lyapunov sense. Wind tunnel tests demonstrate that INDI-QP-V can make the seamless wing morph actively to resist “1-cos” gusts and modify the spanwise lift distribution at the same time. The wing root shear force and bending moment have been alleviated by more than 44% despite unexpected actuator fault and nonlinear backlash. Moreover, during the experiment, all the input constraints were satisfied, the wing shape was smooth all the time, and the control law was executed in real time. Furthermore, as compared with the linear quadratic Gaussian control, the hardware implementation of INDI-QP-V is easier; the robust performance of INDI-QP-V is also superior.@en

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. @en

This paper proposes a nonlinear control architecture for flexible aircraft simultaneous trajectory tracking and load alleviation. By exploiting the control redundancy, the gust and maneuver loads are alleviated without degrading the rigid-body command tracking performance. The proposed control architecture contains four cascaded control loops: position control, flight path control, attitude control, and optimal multi-objective wing control. Since the position kinematics are not influenced by model uncertainties, the nonlinear dynamic inversion control is applied. On the contrary, the flight path dynamics are perturbed by both model uncertainties and atmospheric disturbances; thus the incremental sliding mode control is adopted. Lyapunov-based analyses show that this method can simultaneously reduce the model dependency and the minimum possible gains of conventional sliding mode control methods. Moreover, the attitude dynamics are in the strict-feedback form; thus the incremental backstepping sliding mode control is applied. Furthermore, a novel load reference generator is designed to distinguish the necessary loads for performing maneuvers from the excessive loads. The load references are realized by the inner-loop optimal wing controller, while the excessive loads are naturalized by flaps without influencing the outer-loop tracking performance. The merits of the proposed control architecture are verified by trajectory tracking tasks and gust load alleviation tasks in spatial von K'arm'an turbulence fields. @en

Advancements in aircraft controller design, paired with increasingly flexible aircraft concepts, create the need for the development of novel (smart) adaptive sensing methods suitable for aeroelastic state estimation. A potentially universal and noninvasive approach is visual tracking. However, many tracking methods require manual selection of initial marker locations at the start of a tracking sequence. This study aims to address the gap by investigating a robust machine learning approach for unsupervised automatic labeling of visual markers. The method uses fast DBSCAN and adaptive image segmentation pipeline with hue-saturation-value color filter to extract and label the marker centers under the presence of marker failure. In a comparative study, the DBSCAN clustering performance is assessed against an alternative clustering method, the disjoint-set data structure. The segmentation-clustering pipeline with DBSCAN is capable of running real-time at 250 FPS on a single camera image sequence with a resolution of 1088×600 pixels. To increase robustness against noise, a novel formulation (the inverse DBSCAN, DBSCAN⁻¹ ) is introduced. This approach is validated on an experimental dataset collected from camera observations of a flexible wing undergoing gust excitations in a wind tunnel, demonstrating an excellent match with the ground truth obtained with a laser vibrometer measurement system.

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Recent advancements in aircraft controllers paired with increasingly flexible aircraft designs create the need for adaptive and intelligent control systems. To correctly capture the motion of a flexible aircraft wing and provide feedback to the controller, a large number of states (nodes along the span) must be monitored in real-time. Visual sensing methods carry the promise of flexibility needed for this type of smart sensing and control. However, visual sensing requires capturing and tracking keypoint features (marker tracking), while detecting thereof from a feature-rich image can be a computationally intensive task. The computational effort significantly increases with image size or when an image stereo pair is used to find matching keypoints. In this study, a parallel approach is presented with Threading Building Blocks (TBB), using sub-matrix computations, for extraction of corresponding keypoints from an image-stereo pair, and triangulation with the Direct Linear Transform (DLT) method to reconstruct the 3D position of the object in space. Additional robustness is investigated by implementing a Kalman filter for tracking prediction during the domain transition between the sub-matrices. Furthermore, a flexible simulation framework is set up for smart sensing with a coupled unsteady aeroservoelastic model of a 3D wing and a visual model to test the method for intelligent control feedback in a simulation environment. The methodology is tested in a laboratory environment with a stereo camera setup, and in a virtual environment, where the virtual camera parameters are reconstructed to meet a stereo setup. The proposed approach aims to advance the state-of-the-art in smart sensing, particularly in the context of real-time state estimation of aeroelastic structures and control feedback. The parallel approach shows a significant improvement of speed and efficiency, allowing real-time computation from a live image stream at 50 fps.

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In this study, the design and development of an autonomous morphing wing concept were investigated. This morphing wing was developed in the scope of, the Smart-X project, aiming to demonstrate in-flight performance optimisation. This study proposed a novel distributed morphing concept, with six Translation Induced Camber (TRIC) morphing trailing edge modules, interconnected triangular skin segments joined by an elastomer material to allow seamless variation of local lift distribution along the wingspan. An FSI structural optimisation tool was developed, to achieve this optimised design, and to produce an optimal laminate design of fibre Glass weave material, capable of reaching target shapes and minimise actuation loads. Analysis of the kinematic model of the embedded actuator was performed, and a conventional actuator design was selected to continuously operate at the required load and fulfil both static and dynamic requirements in terms of bandwidth, actuation force and stroke. Preparations were made in this study for the next stage of the Smart-X design, to refine the morphing mechanism design and build a functional demonstrator for wind tunnel testing.

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Advancements in intelligent aircraft controller design, paired with increasingly flexible and efficient aircraft concepts, create the need for the development of novel (smart) adaptive sensing suitable for aeroelastic state estimation. In contrast to rigid states, aeroelastic state estimation requires more measurement points (displacements and forces) across the span to capture the vibrational shapes of the wing undergoing excitations. A potentially universal and non-invasive approach is visual tracking. However, many tracking methods require manual selection of initial marker locations as the start of a tracking sequence. This study is part of a larger study in the field of smart sensing and aims to cover the gap by investigating a robust machine learning approach for unsupervised automatic labelling of visual markers. The method utilizes fast DBSCAN and adaptive image segmentation pipeline with HSV colour filter to extract and label the marker centres under the presence of marker failure. A comparison is made with Disjoint-set data structure for clustering of the data. The segmentation-clustering pipeline with DBSCAN shows the capability to act as a visual tracking method on its own, capable of running real-time at 250 fps on an image sequence of a single camera with a resolution of 1088×600 pixels. To increase the robustness against noise, a novel formulation of DBSCAN better suited against noise, the inverse DBSCAN (DBSCAN ⁻¹), is proposed, allowing to cast the clustering problem into noise filtering problem with an additional MaxPts parameter. Furthermore, observations are made regarding the frequency content of the image pixel intensities across time, and how this can be utilized to estimate the natural frequency of the system and adjust the segmentation-clustering pipeline with a sliding DFT (Discrete Fourier Transform).

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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. @en

n many computer vision applications, it is relevant to know the orientation of the object relative to its symmetry axis. While humans are generally very good at evaluating the reflectional symmetry and relative orientation of objects, constructing a robust symmetry detection pipeline working out of the box is still a challenging task in computer vision. This is particularly the case for irregular objects with skewed geometry and irregular shapes. In this paper, two approaches are presented for symmetry and object orientation detection based on traditional computer vision and Deep Learning. First, an algorithm is presented that allows fast computation of 2-axis reflectional symmetry of contour points and the definition of a radial convex hull, GeConv. The algorithm is tested on 1000+ image dataset from the wind tunnel and flight test experiment. @en

When coupled with additional degrees of freedom, centrifuge-based motion platforms can combine the agility of an hexapod-basedmotion platform with the ability of sustaining higher Glevels and an extended motion space. This combination of motion characteristics is required for realistic simulation of extreme flight scenarios. However, a false and often nauseating sensation of rotation, the so-called Coriolis effect, induced by the central yaw rotation, combined with the simultaneous rotation of the centrifuge cabin (passive Coriolis effect), or pilot’s head (active Coriolis effect), is the main disadvantage of any centrifuge-based motion platform. For this reason, the majority of human centrifuges are used solely as passive G-trainers in relatively short sessions. This paper discusses the development of a novel motion filter which aims to minimize the undesired Coriolis effects, by allowing for small mismatches in the alignment of pitch or roll coordination. Numerical studies showed that this Coherent Alignment Method (COHAM), is capable of reducing the angular accelerations, while constrained to operate within a region of coherent alignment, the Coherent Alignment Zone. In order obtain data to construct the CAZ region, i.e., establish body tilt thresholds in pitch and roll, an experiment was carried out in the Desdemona motion simulator. Results show higher thresholds in pitch and also higher ambiguity in pitch perception. A follow-up study is planned to further develop and experimentally validate our novel, predictive motion filter, based on the established CAZ region. @en

The presence of prolonged microgravity in space has long been known to have a negative impact on the human body such as deterioration of bones and muscles. Rotating space platforms have the potential to mitigate the health risks for prolonged space travel by creating an Earth-like artificial gravity environment for the habitants.@en

The presence of prolonged microgravity in space has long been known to have a negative impact on the human body such as deterioration of bones and muscles. Rotating space platforms have the potential to mitigate the health risks for prolonged space travel by creating an Earth-like artificial gravity environment for the habitants. In this article, Tigran Mkhoyan elaborates on his presentation in the ‘Artificial Gravity’ segment of October’s Asgardia Space Science & Investment Congress (ASIC).@en

Advancements in intelligent aircraft controller design, paired with increasingly flexible and efficient aircrafts concepts, creates the need for the development of novel (smart) adaptive sensing suitable for aeroelastic state estimation. In contrast to rigid states, aeroelastic state estimation requires more measurement points (displacements and forces) across the span to capture the vibrational shapes of the wing undergoing excitations. A potentially universal and non-invasive approach is visual tracking. In this study, a method was developed and tested with the purpose of reducing the effort and cost associated with state estimation of flexible aeroelastic structures. Do so, visual information is used to estimate the elastic states real-time such that these can be provided as feedback to the controller for in-flight performance optimization. The method combines KCF (Kernelized Correlation Filter), which is a purely visual filter, with an augmented Kalman Filter to learn the parameters of the system on-line and account for the dynamics of the motion. Results from wind tunnel-test and flight-test are discussed using an embedded computing system. Further investigations are made on how to learn to predict displacements with deep learning using a convolutional neural network and laser vibrometer data as ground truth.@en

Gilt leather was one of the most fashionable and costly types of wall hangings in the Western world in the 16th to 18th centuries. Despite its appearance, it is not real gold that creates the golden shine, but typically a silver leaf which is coated with an orange-brown lacquer to obtain a golden lustre. Gilt leather has its origins in North-Africa, and the technique was widely practiced in Southern Europe through the middle ages and Renaissance. By the mid-17th century Dutch gilt leather had a similar fame to Delftware and Dutch paintings. Despite this only a small fraction of the large amounts of gilt leather wall hangings produced in Europe has been preserved. Gilt leather is a layered composite of organic and inorganic materials, including leather, animal glue, silver leaf, varnish, oil paint – materials which fall within different conservation disciplines. The aging of gilt leather is characterized by the specific degradations of each of the applied materials, and the possible interactions between them. Some common conservation treatments practiced in the past, such as oiling the leather, have negative side effects, such as gloss and colour change (darkening) and stiffening of the support. Furthermore, most of the surviving gilt leather decorative sections show traces of surface cleaning, such as abrasion, re-varnishing or the application of other protective coatings. Hyperspectral imaging has a wide range of applications in astronomy, biology, chemistry, medicine and quality control. Within the domain of art history, archaeology and conservation, hyperspectral imaging has been used since the 1990’s, mostly for the examination of paintings and manuscripts. It has proved a successful tool for revealing things that are invisible to the naked eye, for example varnish layers, overpaints or underdrawings. In this study the research team investigated a collection of gilt leather objects at SRAL (Stichting Restauratie Atelier Limburg, Maastricht) using an imaging monochromator IMSPECTOR V10E (Specim©, spatial resolution 1300 pixels, wavelength range 400-1000 nm, bandwidth 2.8nm). Scanning of selected case study objects was performed with an automated 3D scanning platform, developed at TU Delft. The objective lens enabled imaging of an approximately 100 mm of field of view. The sample was illuminated by 3 tungsten lamps (30W Halogen) at a 45° angle to avoid specular reflections and to ensure that only diffuse scattering caused by the surface roughness and scattering centres underneath the surface were recorded by the camera. The technique is non-invasive and non-destructive to the studied object. The data was recorded in February 2016 and is currently being analysed using the TIPP software platform, developed at TU Delft. This software platform includes algorithms to perform filtering and un-mixing of hyperspectral data cubes, along with memory management for large data sets and tools for data visualization. Data processing will be performed within the next months, with the objective of mapping areas of surface chemical degradation or change in composition due to earlier conservation treatments. Results will be included in the full paper.@en