This research takes a further step towards the development of an autonomous aeroservoelastic wing concept with distributed flaps. The wing demonstrator, developed within the TU Delft SmartX project, aims to demonstrate in-flight performance optimization and multi-objective control using an over-actuated wing design. To address the challenges posed by the aeroelastic system’s nonlinearities and uncertainties, this paper employs an optimal control method relying on solving the State-Dependent Riccati Equation (SDRE). Geometrical nonlinearities, introduced in the form of plunge and torsion stiffness, make the system state-dependent and unsuitable for linear control methods. Additionally, a backlash model is incorporated to represent the uncertainty of the actuation system. The control strategy is implemented in a multi-objective manner to perform maneuver and gust load alleviation while accounting for the nonlinearities and uncertainties using the SDRE control. Firstly, a numerical sample case is investigated involving a state-dependent and highly non-linear canard aircraft configuration, to assess the ability of the SDRE control method. Then, in a numerical experiment, the effectiveness of the control strategy is evaluated through the nonlinear aeroelastic model. Evaluations are made on the practicality of the control approach, laying a foundation for future static and dynamic wind tunnel experiments with the SmartX-Neo demonstrator.

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With the increasing desire of the aerospace industry to reduce emissions and fuel consumption, morphing wings have gained much interest due to the ability to adapt the wing shape in-flight for improved energy efficiency and aerodynamic performance. Active wing morphing is a technology that can improve aerodynamic performance continuously through different flight phases. However, a multidisciplinary approach is needed, which integrates the design, modelling, sensing and control methodologies in a multi-objective framework, and allows the smart autonomous morphing wing system to adapt its shape autonomously. The SmartX project was initiated for this purpose at the Delft University of Technology, Faculty of Aerospace Engineering, Department of Aerospace Structures and Materials, aiming to investigate the energy-efficient wing concepts through smart wings. This dissertation presents the Development, Realisation & Validation of a smart morphing wing, the SmartX-Alpha, capable of meeting various real-time objectives with distributed seamless morphing modules. This is done through a holistic approach considering all building blocks of a morphing system presented in four Parts of the dissertation. Part I tackles the sensing approach required to reconstruct the shape of the wing in real-time with a vision-based sensing approach. Part II presents the design, development, realisation and experimental testing of a distributed modular morphing concept, SmartX-Alpha. Part III presents the multi-objective control framework developed to meet the gust and manoeuvre load alleviation objective and the real-time shape optimisation strategy to improve online aerodynamic performance. Furthermore, a vision-based control strategy is proposed to mitigate nonlinearities in the actuation system arising from mechanical imperfections. A series of wind tunnel experiments are conducted in the OJF to validate the methodologies on the SmartX-Alpha, ensuring the objectives are satisfied autonomously, in-real time. The final Part, Part IV presents the development of a second wing demonstrator, the SmartX-Neo, with distributed discretised control surfaces incorporating the previous learnings.@en

Morphing structures have acquired much attention in the aerospace community because they enable an aircraft to actively adapt its shape during flight, leading to fewer emissions and fuel consumption. Researchers have designed, manufactured, and tested a morphing wing named SmartX-Alpha, which can actively alleviate loads while achieving the optimal lift distribution. However, the widely existing mechanical imperfections can degrade the performance of the morphing wing and even lead to instabilities. To tackle these issues, this article proposes a vision-based adaptive control approach to actively compensate for mechanical imperfections. In this approach, an incremental model is constructed online to identify the system dynamics using servo commands and vision measurements, and then, nonlinear dynamic inversion control is applied based on the identified model. This data-driven control approach with visual feedback has been validated by real-world experiments on the SmartX-Alpha. The results demonstrate that the vision-based system combined with the proposed control methodology can actively compensate for mechanical imperfections with minimal adjustments to the actual system design. Compared to a controller that only uses a feedforward input-output mapping, this proposed approach improves the system performance and decreases the tracking errors by more than 62% despite disturbances. The results collectively demonstrate the effectiveness of the proposed control system, which sets a foundation for realizing morphing in next-generation aircraft.

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The presented study investigates the design and development of an autonomous morphing wing concept developed in the scope of the SmartX project, which aims to demonstrate in-flight performance optimisation with active morphing. To progress this goal, a novel distributed morphing concept with six translation induced camber morphing trailing edge modules is proposed in this study. The modules are interconnected using elastomeric skin segments to allow seamless variation of local lift distribution along the wingspan. A fluid-structure interaction optimisation tool is developed to produce an optimised laminate design considering the ply orientation, laminate thickness, laminate properties and actuation loads of the module. Analysis of the kinematic model of the integrated actuator system is performed, and a design is achieved, which meets the required continuous load and fulfils both static and dynamic requirements in terms of bandwidth and peak actuator torque with conventional actuators. The morphing design is validated using digital image correlation measurements of the morphing modules. Characterisation of mechanical losses in the actuator mechanism is performed. Out-of-plane deformations in the bottom skin and added stiffness of the elastomer are identified as the impacting factors of the reduced tip deflection.

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This article describes the challenges of integrating smart sensing, actuation, and control concepts into an over-sensed and over-actuated technology integrator. This technology integrator has more control inputs than the expected responses or outputs (over-actuated), and its every state is measured using more than one sensor system (over-sensed). The hardware integration platform is chosen to be a wind tunnel model of a low-speed aircraft wing such that it can be tested in a large university-level wind tunnel. This hardware technology integrator is designed for multiple objectives. The nature of these objectives is aerodynamic, structural, and aeroelastic, or, more specifically; drag reduction, static and dynamics loads control, aeroelastic stability control, and lift control. Enabling technologies, such as morphing, piezoelectric actuation and sensing, and fibre-optic sensing are selected to fulfil the mentioned objectives. The technology integration challenges are morphing, actuation integration, sensor integration, software and data integration, and control system integration. The built demonstrator shows the intended level of technology integration.@en

Inspired by nature, smart morphing technologies enable the aircraft of tomorrow to sense their environment and adapt the shape of their wings in flight to minimize fuel consumption and emissions. A primary challenge on the road to this feature is how to use the knowledge gathered from sensory data to establish an optimal shape adaptively and continuously in flight. To address this challenge, this article proposes an online black-box aerodynamic performance optimization architecture for active morphing wings. The proposed method integrates a global online-learned radial basis function neural network (RBFNN) model with an evolutionary optimization strategy, which can find global optima without requiring in-flight local model excitation maneuvers. The actual wing shape is sensed via a computer vision system, while the optimized wing shape is realized via nonlinear adaptive control. The effectiveness of the optimization architecture was experimentally validated on an active trailing-edge (TE) camber morphing wing demonstrator with distributed sensing and control in an open jet wind tunnel. Compared with the unmorphed shape, a <inline-formula> <tex-math notation="LaTeX">$7.8\%$</tex-math> </inline-formula> drag reduction was realized, while achieving the required amount of lift. Further data-driven predictions have indicated that up to <inline-formula> <tex-math notation="LaTeX">$19.8\%$</tex-math> </inline-formula> of drag reduction is achievable and have provided insight into the trends in optimal wing shapes for a wide range of lift targets.

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This paper presents an experimental method to detect in-situ the location of transition on a multi-segmental trailing edge camber morphing wing during synchronous and asynchronous morphing. The wing consists of six independently morphing segments with two of the segments instrumented with eight embedded piezoelectric sensors distributed uniformly along the chord. Using suitable data processing, each of the sensors gives a signal that can be used to determine the state of the boundary layer (laminar, transitional, turbulent) at the location of that sensor. The results showed that synchronous morphing can substantially shift the location of transition, up to 20% of the chord length for angles of attack below 9°. Differences in the location of transition up to 5% are found between the near-root and near-tip segment. Using a dedicated data processing approach, the location of transition could be reconstructed in case of complex asynchronous morphing involving one to five segments. The results show a shift in the location of transition when morphing neighboring segments, but also show that non-neighboring segments have a minimal effect. This sensing method holds significant promise for online advanced morphing control to delay transition and thereby reducing skin friction drag.

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This study investigated the design and development of an autonomous aeroservoelastic wing concept with distributed flaps. This wing demonstrator was developed in the scope of the SmartX project, aiming to demonstrate in-flight performance optimization and multi-objective control with over-actuated wing designs. Following a successful test campaign with a previous wing design based on active morphing, this study aims to develop an over-actuated aeroelastic wing design suitable for aeroelastic control, including flutter suppression, maneuver and gust load alleviation. A decentralized control architecture is developed for the over-actuated and over-sensed system, allowing efficient sensing data processing and control algorithms. Aerodynamic and structural analyses are performed to determine actuator torque requirements and actuation mechanism design. Furthermore, buckling analysis is performed to size the wing structure. A state-space aeroelastic dynamic model is established to analyze the gust response and control effectiveness of the wing. It is established that a linear quadratic regulator significantly improves the closed-loop performance. Furthermore, the hypotheses are confirmed that fast actuation improves load alleviation performance and high-frequency disturbance rejection effectiveness. The manufacturing and integration of the wing demonstrator are discussed, which lay a foundation for future static and dynamic wind-tunnel experiments. @en

Morphing is a promising bio-inspired technology, with the potential to make aircraft more economical and sustainable through adaptation of the wing shape for best efficiency at any flight condition. This paper proposes an online black-box performance optimization strategy for a seamless wing with distributed morphing control. Pursuing global performance, the presented method integrates a global radial basis function neural network (RBFNN) surrogate model with a derivative-free evolutionary optimization algorithm. The effectiveness of the optimization strategy was validated on a vortex lattice method (VLM) aerodynamic model of an over-actuated morphing wing augmented by wind tunnel experiment data. Simulations show that the proposed method is able to control the morphing shape and angle of attack to achieve various target lift coefficients with better aerodynamic efficiency than the unmorphed wing shape. The global nature of the on-board model allows the presented method to find shape solutions for a wide range of target lift coefficients without the need for additional model excitation maneuvers. Compared to the unmorphed shape, up to 14.6% of lift-to-drag ratio increase is achieved.@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 loops: position control, flight path control, attitude control, and optimal multi-objective wing control. Because 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 implemented. 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, whereas 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 in spatial von Kármán turbulence fields.

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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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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

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

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

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