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This work proposes an analysis of the capability of three deep learning models—the feedforward neural network (FFNN), long short-term memory (LSTM) network, and physics-informed neural network (PINN)—to identify the parameters of a flexible fixed-wing aircraft using in-flight data. These neural networks, composed of multiple hidden layers, are evaluated for their ability to perform system identification and to capture the nonlinear and dynamic behavior of the aircraft. The FNN and LSTM models are compared to assess the impact of temporal dependency learning on parameter estimation, while the PINN integrates prior knowledge of the system’s governing of ordinary differential equations (ODEs) to enhance physical consistency in the identification process. The objective is to exploit the generalization capability of neural network-based models while preserving the accurate estimation of the physical parameters that characterize the analyzed system. The neural networks are evaluated for their ability to perform system identification and capture the nonlinear behavior of the aircraft. The results show that the FFNN achieved the best overall performance, with average Theil’s inequality coefficient (TIC) values of 0.162 during training and 0.386 during testing, efficiently modeling the input-output relationships but tending to fit high-frequency measurement noise. The LSTM network demonstrated superior noise robustness due to its temporal filtering capability, producing smoother predictions with average TIC values of 0.398 (training) and 0.408 (testing), albeit with some amplitude underestimation. The PINN, while successfully integrating physical constraints through pretraining with target aerodynamic derivatives, showed more complex convergence, with average TIC values of 0.243 (training) and 0.475 (testing), and its estimated aerodynamic coefficients differed significantly from the conventional values. All three architectures effectively captured the coupled rigid-body and flexible dynamics when trained with distributed wing sensor data, demonstrating that neural network-based approaches can model aeroelastic phenomena without requiring explicit high-fidelity flexible-body models. This study provides a comparative framework for selecting appropriate neural network architectures based on the specific requirements of aircraft system identification tasks.

Many tracking control solutions proposed in the literature rely on various forms of tracking error signals at the expense of possibly overlooking other dynamic criteria, such as optimizing the control effort, overshoot, and settling time, for example. In this article, a model-free control architectural framework is presented to track reference signals while optimizing other criteria as per the designer’s preference. The control architecture is model-free in the sense that the plant’s dynamics do not have to be known in advance. To this end, we propose and compare four tracking control algorithms which synergistically integrate a few machine learning tools to compromise between tracking a reference signal and optimizing a user-defined dynamic cost function. This is accomplished via two orchestrated control loops, one for tracking and one for optimization. Two control algorithms are designed and compared for the tracking loop. The first is based on reinforcement learning while the second is based on nonlinear threshold accepting technique. The optimization control loop is implemented using an artificial neural network. Each controller is trained offline before being integrated in the aggregate control system. Simulation results of three scenarios with various complexities demonstrated the effectiveness of the proposed control schemes in forcing the tracking error to converge while minimizing a pre-defined system-wide objective function.

Classical gradient-based approximate dynamic programming approaches provide reliable and fast solution platforms for various optimal control problems. However, their dependence on accurate modeling approaches poses a major concern, where the efficiency of the proposed solutions are severely degraded in the case of uncertain dynamical environments. Herein, a novel online adaptive learning framework is introduced to solve action-dependent dual heuristic dynamic programming problems. The approach does not depend on the dynamical models of the considered systems. Instead, it employs optimization principles to produce model-free control strategies. A policy iteration process is employed to solve the underlying Hamilton–Jacobi–Bellman equation using means of adaptive critics, where a layer of separate actor-critic neural networks is employed along with gradient descent adaptation rules. A Riccati development is introduced and shown to be equivalent to solving the underlying Hamilton–Jacobi–Bellman equation. The proposed approach is applied on the challenging weight shift control problem of a flexible wing aircraft. The continuous nonlinear deformation in the aircraft’s flexible wing leads to various aerodynamic variations at different trim speeds, which makes its auto-pilot control a complicated task. Series of numerical simulations were carried out to demonstrate the effectiveness of the suggested strategy.

The aerodynamic model of flexible wing aircraft is highly nonlinear with continuously time-varying dynamics under kinematic constraints. The nonlinearities stem from the aerodynamic forces and continuous deformations in the flexible wing. In spite of the various experimental attempts and theoretical setups that were made to model these dynamics, an accurate formulation was not achieved. The control paradigms of the aircraft are concerned with the electro-mechanical coupling between the pilot and the wing. It is challenging to design a flight controller for such aircraft while complying with these constraints. In this paper, innovative machine learning technique is employed to design a robust online model-free control scheme for flexible wing aircraft. The controller maintains internal asymptotic stability for the aircraft in real-time using selected set of measurements or states in uncertain dynamical environment. It intelligently incorporates the varying dynamics, geometric parameters, and physical constraints of the aircraft into optimal control strategies. The adaptive learning structure employs a policy iteration approach, taking advantage of Bellman optimality principles, to converge to an optimal control solution for the problem. Artificial neural networks are adopted to implement the adaptive learning algorithm in real-time without prior knowledge of the aerodynamic model of the aircraft. The control scheme is generalized and shown to function effectively for different pilot/wing control mechanisms. It also demonstrated its ability to overcome the undesired stability problems caused by coupling the pilot’s dynamics with the flexible wing’s frame of motion.

The trailing edge deformable wing is one of the main development directions of future aircraft design. The technology of active camber of wing trailing edge can significantly improve the aerodynamic performance of aircraft. In this paper, the motor drives the parabolic crankshaft and the distributed crankshaft drives the flexible skin of the trailing edge of the wing, which can realize the deflection of the trailing edge of the wing at a large angle of 0~30 degrees. At the same time, the surface of the wing is continuous and smooth. Moreover, the aerodynamic simulation results of the deformed wing show that the trailing edge deflection is beneficial to increase the lift. It can increase the maximum lift coefficient by 2 times and the maximum lift drag ratio by 18%. Adaptive flexible wing technology is applied to rocket sleds with side wings. This technology can increase the downward pressure of the side wing of the rocket block and improve the operation safety.

Morphing wings allow aircrafts to exhibit good aerodynamic performance at multiple points within a flight envelope. However, forthcoming aircraft designed for cross-speed domain operations impose fresh demands, particularly in the realm of wing shape adjustments, such as transient morphing. Inspired by the diving process of the northern gannet, this study proposes a morphing-wing eight-bar mechanism for transient variable sweeping under high-speed conditions. A comprehensive analysis of the rigid-flexible coupling dynamic models of the mechanism is developed by employing the finite-element floating frame of reference formulation. To numerically solve the dynamic equations, a concise reduced-order algorithm that included static equilibrium compensation is introduced. The maximum dynamic stress encountered during the morphing process at various speeds and time ratio at which each link is subjected to the maximum stress, referred to as the maximum stress-time ratio of each link, are evaluated. The results show that the maximum dynamic stress and maximum stress–time ratio serve as effective measures of the performance of the mechanism under high-speed working conditions. Although the proposed eight-bar variable-sweepback-wing mechanism requires further structural optimization to achieve superior high-speed performance, it demonstrates potential for high-speed applications.

As the only flying mammal in nature, bats have superb flight skills and aerodynamic characteristics that have been the subject of research by scholars from all over the world. In recent years, the research on bionic flapping-wing aircraft has made good progress. However, such research mostly uses birds or insects as the research objects, and there are few studies on bat-imitating flapping-wing aircraft. This paper combines the characteristics of bats’ flexible wings to model and analyze the aerodynamic theory and parameters of the flexible wings of bat-like flapping aircraft. The longitudinal dynamic and kinematic model design of bat-like flapping aircraft is based on the pitch angle of LQR. In terms of height control, the controller uses energy control methods to complete the closed-loop longitudinal channel control of the bat-like flapping aircraft. Finally, this study performed the simulation and flight experimentation of the designed bat-like flapping aircraft, demonstrating the correctness of this system.

Shock buffet on wings encountered in edge-of-the-envelope transonic flight remains an unresolved and disputed flow phenomenon, challenging both fundamental fluid mechanics and applied aircraft aerodynamics. Its dynamics is revealed through the interaction of spanwise shock-wave oscillations and intermittent turbulent boundary-layer separation. Resulting unsteady aerodynamic loads, and their mutual working with the flexible aircraft structure, need to be accounted for in establishing the safe flight envelope. The question of global instability leading to this flow unsteadiness is addressed herein. It is shown for the first time on an industrially relevant configuration that the dynamics of a single unstable oscillatory eigenmode plays a prominent role in near-onset shock buffet on a quasi-rigid wing. Its three-dimensional spatial structure, previously inferred both from experiment and time-marching simulation, describes a spanwise-localised pocket of shear-layer pulsation synchronised with an outboard-propagating shock oscillation. The results also suggest that the concept of a critical global shock-buffet mode commonly reported for two-dimensional aerofoils also applies to three-dimensional finite and swept wings, albeit different modes at play. Specifically, the modern wing design, NASA Common Research Model, with publicly available geometry and experimental data for code validation is studied at a free-stream Mach number of 0.85 with Reynolds number per reference chord of$5.0\\times 10^{6}$and varying angle of attack between 3. 5 ° and 4. 0 ° targeting the instability onset. Strouhal number at instability onset just above 3. 7 ° is approximately 0.39. At the same time, a band of eigenmodes shows reduced decay rate in the Strouhal-number range of 0.3 to 0.7, with additional unstable oscillatory modes appearing beyond onset. Importantly, those emerging modes seem to discretise the continuous band of medium-wavelength modes, as recently reported for infinite swept wings using stability analysis, hence generalising those findings to finite wings. Through conventional time-marching unsteady simulation it is explored how the critical linear eigenmode feeds into the nonlinearly saturated limit-cycle oscillation near instability onset. The established numerical strategy, using an iterative inner–outer Krylov approach with shift-and-invert spectral transformation and sparse iterative linear solver, to solve the arising large-scale eigenvalue problem with an industrial Reynolds-averaged Navier–Stokes flow solver means that such a practical non-canonical test case at a high-Reynolds-number condition can be investigated. The numerical findings can potentially be exploited for more effective unsteady flow analysis in future wing design and inform routes to flow control and model reduction.

Small flexible-wing aircraft are vulnerable to gusts due to their low inertia and operating regime at low-Reynolds-number regimes, compromising flight stability and mission reliability. This paper introduces a novel active gust alleviation device (AGAD) installed at the wingtip, which works in concert with the conventional tail-plane to form a multi-surface control system. To coordinate these surfaces optimally, a quasi-static aeroelastic aircraft model is established, and a linear–quadratic regulator (LQR) controller is designed. A key innovation is the integration of an extended state observer (ESO) to estimate the unmeasurable, gust-induced angle of attack in real time, allowing the LQR to effectively counteract unsteady disturbances. Comparative simulations against a baseline (tail-plane-only control) demonstrate the superiority of the combined AGAD-tail strategy: the peak gust responses in pitch angle and normal acceleration are reduced by over 57% and 20%, respectively, while structural loads at the wing root are also significantly attenuated. Furthermore, the AGAD enhances maneuverability, reducing climb time by 20% during a specified maneuver. This study confirms that the integrated AGAD and LQR-ESO framework provides a practical and effective solution for enhancing both the stability and agility of small flexible aircraft in gusty environments, with direct benefits for applications like precision inspection and monitoring.

An evaluation of the commercial transport aircraft developed over the past decades evidences an increasing trend toward the use of high aspect-ratio wings. This trend is justified by the well-known effect of slender wings in reducing fuel consumption, leading to lower operational costs and a milder environmental impact. There are many studies about the effects of geometric nonlinearities on aeroelastic behavior of very flexible wings in symmetrical maneuvers. However, geometric nonlinearities may also significantly affect the aeroelastic behavior of the wing under non-symmetrical conditions, especially when ailerons are deflected. Within this context, this work presents a static fluid–structure interaction approach to evaluate the rolling characteristics of very flexible wings. First, a modified version of the very flexible Pazy Wing from Aeroelastic Prediction Workshop (AEPW-3) is proposed, now equipped with ailerons. Next, a fluid–structure interaction tool that couples a full potential aerodynamic solver with an implicit nonlinear structural solver is presented to allow simulations of wings with deflected ailerons. The presented method is applied to the modified Pazy wing considering multiple linear and nonlinear structural analyses, for different aileron deflection angles. The results show that when geometric nonlinearity effects are considered, the aileron effectiveness tends to decrease as the structural flexibility increases. On the other hand, if geometric nonlinearities are neglected, the aileron effectiveness falsely enhances as the wing flexibility rises.