This article is devoted to the antibump switched linear parameter varying (sLPV) controller design for morphing aircraft under delayed scheduling variables (or parameters), which is typically caused by lagging measurements of the morphing extent. Such delayed scheduling is formulated as the control disturbance and the asynchronous control in the sLPV scheme, according to whether the current mode governed by scheduling variables is correctly detected or not. The persistent dwell time (PDT) switching signals are utilized in this article to describe inherent slow and rapid switching phenomena for steady flight and fast morphing, respectively, which is more applicable than the conventional average dwell time (DT) or DT and covers them as special cases. By adopting the detected-mode-based Lyapunov functions and a smooth function, the stability condition is obtained for the underlying system, upon which the antibump sLPV controller allowing for delayed scheduling is designed, in contrast to the existing studies that simply ignore the detection lag to allow the use of overlapped partitions and scheduling-variable-dependent Lyapunov functions for different modes. By an aircraft with a variable-sweep wing and an aircraft with a deformable wingspan, the effectiveness and the superiority of the proposed approach are demonstrated via simulations.

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This paper presents a novel approach to suppress gust-induced limit cycle oscillations (LCOs). The proposed method integrates incremental nonlinear dynamic inversion (INDI) and robust nonlinear model predictive control (NMPC) with tightened constraints. The INDI method estimates and actively rejects gusts, resulting in a reduced disturbance residue. The upper bound of the disturbance residue can be estimated either online or offline and is used to bound the maximum state deviation caused by uncompensated disturbances, thereby imposing tightened constraints for the NMPC scheme to improve the robustness of constraint satisfaction and stabilize the system. Simulation results on a 2-D nonlinear aeroservoelastic wing demonstrate that the proposed method stabilizes the nonlinear system and reduces the disturbed peak plunge and pitch motions by up to 20.69% and 33.70%, respectively. Additionally, the method mitigates the conservativeness of the robust NMPC, with an 81.67% reduction in the offline estimated upper bound of the disturbance residue. The online estimation of the disturbance residue captures its peak value while further relaxing the tightened constraint set when disturbance effects are small. The proposed control scheme effectively suppresses gust-induced LCO motions and reduces the conservativeness of the tightened constraint sets used in the robust NMPC.@en

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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This paper contributes towards the development of a reduced-order modelling methodology for nonlinear, unsteady aerodynamic loads for the active control of transonic aeroelastic flutter. To this end, a 1-DOF torsional NACA0012 airfoil is chosen as the test configuration. The aim is to develop the reduced-order model in nonlinear state-space form to be used in active control scenarios. Hence, a nonlinear coupled differential equation that captures the shock dynamics. The underlying hypothesis of this work is that, once these aerodynamic effects are included in the low-order model, the nonlinear trend in the flutter stability boundary, specifically in the transonic regime, will be predicted purely based on first principles, without the need for numerical or experimental corrections. In this work, we observe that the aeroelastic system could become prematurely unstable as soon as the aerodynamic flow field undergoes a Hopf bifurcation. For low amplitude airfoil pitching below a certain threshold, the aerostructural system is seen to exhibit a coupled oscillator behaviour that has an exact linear analytical formulation. The analytical formulation thus produces an accurate prediction whilst being orders of magnitude faster than the numerical simulation. @en

In modern aircraft design, electro-mechanical actuators are increasingly being considered as an alternative to conventional, hydraulic actuation systems for flight control surfaces. While offering advantages in terms of weight reduction and increased efficiency, these actuators are also characterised by a higher sensitivity to nonlinear effects. Actuator models can strongly affect the effectiveness of control function such as gust load alleviation or flutter suppression, it is essential to correctly understand, model and identify nonlinearities in the actuator response, as well as to integrate nonlinear actuator models into aeroservoelastic models. This contribution explores the nonlinear effects of rate and acceleration limits in actuation systems, focusing on the actuator steady-state response to sinusoidal input and the effectiveness of closed-loop control systems. The saturation regimes determined by rate and acceleration limits are investigated, and analytical formulations are derived for the nonlinear actuator response and the boundaries of these regimes within the two-dimensional parameter space defined by non-dimensional rate and acceleration limits. Describing functions for each regime are determined in a closed form, establishing the relationship between actuator input and output in the frequency domain. Combined rate and acceleration limits are found to induce a low-pass filter behaviour in the actuator, with a -40 dB/decade roll-off, and can lead to nonsmooth phase dependence on frequency. The describing functions of combined rate and acceleration limits are applied to the analysis of an aeroservoelastic wing model developed for gust load alleviation (GLA) purposes. The effect of the actuator limits is investigated by evaluating the onset point of the nonlinear behaviour and an equivalent describing function for the entire actuator-plant-control feedback loop. The resulting findings illustrate that rate and acceleration limits can substantially affect the performance of closed-loop systems, leading to phenomena such as jump resonances when partial-to-full saturation regime transitions occur, and thereby constraining the effective frequency range of the GLA control systems. @en

This paper aims to explore the advantages offered by machine learning (ML) for dimensionality reduction of nonlinear transonic aerodynamics. Three ML techniques are evaluated in terms of their ability to generate interpretable low-dimensional manifolds of the transient pressure distributions over a NACA4412 airfoil equipped with a flap. These ML techniques are Kernel Principle Component Analysis (kPCA), Locally Linear Embedding (LLE), and t-distributed Stochastic Neighbourhood Embedding (t-SNE). Initial investigations are also carried out to evaluate the performance of Artificial Neural Networks (ANNs). Three transient aerodynamic test cases are evaluated. First, a static aerodynamic transient analysis. Second, pitching and heaving airfoils in terms of prescribed sinusoidal displacements. Lastly, the airfoil geometry is adapted to include a flap under sinusoidal actuation. The snapshots forming the ground truth are obtained from unsteady CFD simulations. The preliminary results of this study reveal that patterns exist in low-dimensional nonlinear manifolds. Furthermore, unsupervised learning techniques are seen to outperform supervised neural networks in terms of both training cost and reconstruction accuracy. Promising reconstruction capabilities are observed with unsupervised learning.@en

Considerable growth in the number of passengers and cargo transported by air is predicted. Moreover, aircraft noise and climate impact become increasingly important factors in aircraft design. These existing challenges in aviation boost interest in the design of innovative aircraft configurations. One of these configurations is a V-shaped flying wing named the Flying-V. This work aims at developing a flight control system for the Flying-V that can be used to improve the stability and handling qualities of the aircraft. Prior work shows that the Flying-V is not able to adhere to all stability and handling quality requirements at the forward and aft centre of gravity location during cruise and approach. This paper illustrates how an Incremental Nonlinear Dynamic Inversion flight control system can be used to improve the stability and handling qualities of the aircraft. Furthermore, the robustness of the flight control system is assessed by analysing the effects of aerodynamic uncertainty on the attitude tracking error of the Flying-V. Upon implementation of the flight control system, this research shows that the eigenmodes become stable. Besides that, the flight control system is proved to be robust against aerodynamic uncertainty.

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This paper focuses on the attitude control and propellant slosh suppression of aeroelastic launch vehicles in a turbulent atmosphere. For a ve-degree pitch-angle block command, the tracking performance of the selected Incremental Non-Linear Dynamic Inversion Sliding Mode Controller (INDI-SMC) shows excellent tracking performance. However, turbulence still inevitably leads to oscillatory behaviour in the swivel command. Various lter designs have been implemented to improve the smoothness of INDI-SMC. Using either a notch or band-pass lter in the sensor-feedback loops of pitch angle and pitch rate only marginally reduced the swivel oscillations, but did not solve the problem for the rigid-body control. For the exible launcher with slosh dynamics, ltering of the sensor-feedback signals reduced the oscillations in swivel command, and elastic and slosh motion signi cantly, but could not completely remove them. The preliminary design of a rigid-body state observer has been included, and the results show that the INDI-SMC controller remains stable in the presence of engine dynamics, sloshing, exible modes, input errors due to the use of rigid-body and slosh-state observers, while ying in a turbulent wind field.

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This paper proposes an incremental nonlinear control method for an aeroelastic system’s gust load alleviation and active flutter suppression. These two control objectives can be achieved without modifying the control architecture or the control parameters. The proposed method has guaranteed stability in the Lyapunov sense and also has robustness against external disturbances and model mismatches. The effectiveness of this control method is validated by wind tunnel tests of an active aeroelastic parametric wing apparatus, which is a typical wing section containing heave, pitch, flap, and spoiler degrees of freedom. Wind tunnel experiment results show that the proposed nonlinear incremental control can reduce the maximum gust loads by up to 46.7% and the root mean square of gust loads by up to 72.9%, while expanding the flutter margin by up to 15.9%.

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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

In this paper, we establish an event-triggered intelligent control scheme with a single critic network, to cope with the optimal stabilization problem of nonlinear aeroelastic systems. The main contribution lies in the design of a novel triggering condition with input constraints, avoiding the Lipschitz assumption on the inverse hyperbolic tangent function. Based on an improved weight updating criterion that eliminates the requirement of initial admissible control, the control law is obtained approximately by online training of a single critic network. The Lyapunov stability and the Zeno phenomenon of the closed-loop system are analysed. The feasibility of the established algorithm is verified by applying it to an optimal stabilization task of a nonlinear aeroelastic system. The results reveal that the developed approach can handle input-constrained optimal control problems, with performance comparable to the time-based method that updates control inputs at each instant, while reducing the computational and communication's load.

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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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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

The sensor-based Incremental Nonlinear Dynamic Inversion (INDI) control has shown promising robustness in the aerospace research field. This control framework only requires a partial knowledge of plant (control effectiveness) because of its usage of angular accelerations and actuator output measurements. However, there are still un-negligible uncertainties of the control effectiveness model in the flight control system, especially when the aircraft is subjected to structural damage/actuator faults. This paper shows that the conventional INDI control fails to satisfy the sufficient conditions for closed-loop stability in the presence of severe damage. Therefore, this paper also proposes a predictor-based gain adaptive INDI control (named PGA-INDI) which can successfully deal with control effectiveness parametric errors caused by structural damage, actuator faults, and model uncertainties. Various simulations using a public aircraft model have demonstrated the effectiveness of the proposed approach.

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This paper proposes an incremental nonlinear control method for aeroelastic sys- tem gust load alleviation and active flutter suppression. These two control objectives can be achieved without modifying the control architecture or the control parameters. The proposed method has guaranteed stability in the Lyapunov sense and also has robustness against external disturbances and model mismatches. The effectiveness of this control method is validated by wind tunnel tests of an active aeroelastic parametric wing apparatus, which is a typical wing section containing heave, pitch, flap, and spoiler degrees of freedom. Wind tunnel experiment results show that the proposed nonlinear incremental control can reduce the maximum gust loads by up to 46.7% and the root mean square of gust loads by up to 72.9%, while expanding the flutter margin by up to 15.9%.@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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This article exposes that although some sensor-based nonlinear fault-tolerant control frameworks including incremental nonlinear dynamic inversion control can passively resist a wide range of actuator faults and structural damage without requiring an accurate model of the dynamic system, their stability heavily relies on a sufficient condition, which is unfortunately violated if the control direction is unknown. Consequently, it is proved in this article that no matter, which perturbation compensation technique (adaptive, disturbance observer, sliding-mode) is implemented, none of the existing nonlinear incremental control methods can guarantee closed-loop stability. Therefore, this article proposes a Nussbaum function-based adaptive incremental control framework for nonlinear dynamic systems with partially known (control direction is unknown) or even completely unknown control effectiveness. Its effectiveness is proved in the Lyapunov sense and is also verified via numerical simulations of an aircraft attitude tracking problem in the presence of sensing errors, parametric model uncertainties, structural damage, actuator faults, as well as inversed and unknown control effectiveness.

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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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