This paper presents a transformation-based robust nonlinear control design framework based on the concepts of Incremental Nonlinear Dynamic Inversion (INDI) and quasi-Linear Parameter-Varying (q-LPV) control. The duality between these popular control design paradigms is investigated. Control-oriented q-LPV model representations of (I)NDI-based closed-loop systems are presented for various inversion strategies, which creates a basis for robust synthesis and analysis of (I)NDI-based designs in the LPV sense. This includes extensions to singular perturbations that limit exact inversion in reality. The presented approach is demonstrated in a design case study for a nonlinear aeroservoelastic system, where INDI-based controllers are synthesized and quantitatively compared with direct q-LPV control based on linear fractional transformations.

We use feature-based modeling framework for control of loads on aeroelastic wings in the presence of unsteady wind gust disturbances, leveraging interpretable reduced-order-model system identification techniques without relying on black-box machine learning techniques. Unlike prior formulations, we explicitly include the dominant disturbance, such as a gust, as a control input within the model, allowing the controller to respond adaptively and in anticipation to external forcing. To this end, model predictive control (MPC) could be used in the low dimensional latent space of features, whose dynamics are identified partly by sparse identification of nonlinear dynamics with control (SINDYc) and linear parameter-varying system (LPV). As a proof of concept, the methodology is applied to system identification of smart vortex generators (SVGs) for mitigating transient gust loads on an aeroelastic wing section in CFD simulations. This methodology offers a promising path toward real-time mitigation of atmospheric disturbances in next-generation flight systems.

Ultraefficient, high-aspect-ratio wings offer a promising solution for reducing emissions in next-generation aircraft. However, these designs are sensitive to atmospheric disturbances and prone to instability. While active control strategies can mitigate structural loads and stabilize the system, their development is challenging due to the uncertain and time-varying nature of aeroelastic systems. This article addresses these challenges with a direct, adaptive, data-driven approach. The proposed data-enabled policy optimization algorithm leverages sample covariance to directly learn and adapt control strategies from a single batch of persistently exciting, closed-loop input–output data. A forgetting factor mechanism enhances adaptability to time-varying dynamics during operation. The algorithm is explicit and recursive, requiring only a single step of projected gradient descent per sample, improving computational efficiency and enabling real-time application. Numerical simulations demonstrate that the proposed algorithm effectively suppresses unstable flutter, alleviates structural loads, adapts to dynamic time variations, and minimizes control effort—all without requiring prior knowledge of system dynamics or disturbances.

Quadrotors can carry slung loads to hard-to-reach locations at high speed. Given that a single quadrotor has limited payload capacities, using a team of quadrotors to collaboratively manipulate the full pose of a heavy object is a scalable and promising solution. However, existing control algorithms for multilifting systems only enable low-speed and low-acceleration operations because of the complex dynamic coupling between quadrotors and the load, limiting their use in time-critical missions such as search and rescue. In this work, we present a solution to substantially enhance the agility of cable-suspended multilifting systems. Unlike traditional cascaded solutions, we introduce a trajectory-based framework that solves the whole-body kinodynamic motion planning problem online, accounting for the dynamic coupling effects and constraints between the quadrotors and the load. The planned trajectory is provided to the quadrotors as a reference in a receding-horizon fashion and is tracked by an onboard controller that observes and compensates for the cable tension. Real-world experiments demonstrate that our framework can achieve at least eight times greater acceleration than state-of-the-art methods to follow agile trajectories. Our method can even perform complex maneuvers such as flying through narrow passages at high speed. In addition, it exhibits high robustness against load uncertainties and wind disturbances and does not require adding any sensors to the load, demonstrating strong practicality.

In nature, birds can intelligently adapt their wing shapes to their environment. This paper aims to replicate this capability by designing an online data-driven aerodynamic performance optimization framework for an unconventional morphing aircraft. Compared to state-of-the-art methods, the proposed framework efficiently finds global optima with reduced computational load when addressing time-varying, nonlinear, and non-convex problems. It also demonstrates enhanced adaptability to unforeseen scenarios. In the event of a sudden actuator fault, the algorithm can automatically detect the fault, adapt the onboard data-driven model, and continue performing optimization and trimming tasks using the remaining healthy actuators. Additionally, the paper addresses the optimal number of actuators within a morphing surface, considering the tradeoff between optimization performance and the weight penalty. High-fidelity simulations demonstrate that through active morphing, the proposed framework achieves drag reductions of 1.9–4.9 % during cruise and up to 12.6 % at higher operational lift coefficients (due to heavier weight and lower speed), resulting in an overall drag reduction of 2.97 % over a typical flight cycle, which corresponds to fuel savings of approximately 150 kg/h. This research represents a significant advancement in sustainable aviation, contributing to reduced fuel consumption, lower emissions, and improved fault tolerance for next-generation aircraft.

Nonlinear Dynamic Inversion (NDI) has a long and successful history of research and development. The need for gain scheduling for nominal performance may be alleviated with the NDI method, which is accompanied by developmental benefits in terms of design modularity and transparency. However, the robustness of NDI-based control laws remains dependent on the nature of the open-loop plant. In this paper, a design and analysis framework based on quasi Linear Parameter-Varying (q-LPV) system theory is proposed that systematically considers this aspect across nonlinear operating regimes. The q-LPV model framework is presented in the context of robust hybrid incremental NDI control design, which incorporates inversion error compensation in addition to baseline model predictions. Based on a design case study for a simulated aeroservoelastic system, it is shown how systematic gain scheduling of the related inversion compensation design parameters can be performed with the proposed approach.

Identifying individual control effectiveness parameters for aircraft with several distributed control surfaces can be efficiently performed using multisine inputs. While commonly used in flight testing, these inputs were used to identify control effectiveness models for the half version of the subscale Flying V aircraft through wind tunnel experiments, and these were compared with control effectiveness parameters obtained from static deflections. The control effectiveness parameters estimated through multisine inputs were consistently higher than those obtained from static deflections. This occurs due to inertial forces induced by structural vibrations of the wing in the airstream as the multisine excitation frequency approaches the first natural frequency of the wing. The effects of inertial forces when using multisine inputs are not highlighted in the literature, and bring important consequences for using these inputs on flexible aircraft and wings.

This paper aims to develop a reduced-order modelling methodology for nonlinear, unsteady, aerodynamic loads for active control transonic aeroelastic instabilities. To this end, a NACA0012 airfoil equipped with a flap is chosen as the test configuration. The aim here is to understand the interaction between the transonic shock dynamics and flap actuation at various amplitudes and frequencies. The high-fidelity simulations are carried out for two angles of attack, i.e. a = 0.0°, 4.0°. It is found that transonic buffet characteristics significantly change with airfoil geometry. Additionally, the flap is seen to be ineffective in the separated flow regions, thereby making the Ci-fi slopes highly nonlinear. However, increasing the frequencies of flap oscillations, increases flap effectiveness, increases control over buffet motion and moves towards linear lift responses. Furthermore, we also evaluate the performance of several Bayesian Filters that are crucial in the state-estimation process of the active control of nonlinear systems. It is observed that nonlinear filters such as Unscented Karman Filter perform better than the traditional linear Kalman Filter as system response to flap actuation becomes nonlinear in the presence of separated boundary layer.

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.

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.

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.

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.

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.

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%.

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.

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.

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.

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.