Flexible Wing Aircraft Research Articles

Fast unsteady method for non-linear aeroelastic responses of flexible aircraft wings encountering wind gusts

Given the current context of changes in aeronautics to reduce emissions, it is also necessary to modernise the computation methods to anticipate future cases where disciplines which are now calculated separately (i.e. manoeuvers and gusts) should be computed at the same time including flexible effects and using a time-domain approach. In this work, a static aeroelasticity formulation is adapted to compute wind gust loads. This static method uses aerodynamic matrices to calculate an effective angle of attack (used to recover the local pressure coefficients) from a structural deformation. The approach has been to define this deformation including unsteady effects influence in order to use the same formulation as the static case. Three gust cases (two unsteady and one quasi-steady) have been tested in a rectangular wing, and the proposed method has been compared to the aeroelastic high-fidelity solution and to an uncorrected version of the Doublet Lattice Method (Nastran Solution 146). The proposed solution benefits from the use of the lookup tables to accurately estimate the peak lift coefficient value (maximum error of 6.7%) at least 2.5 times faster than the Doublet Lattice Method. Nevertheless, using a limited model with only two degrees of freedom prevents the proposed method from capturing complex dynamics coming from highly unsteady gust excitation or from aerodynamic instabilities.

A transfer learning-physics informed neural network (TL-PINN) for vortex-induced vibration

Vortex-induced vibration (VIV) is a typical nonlinear fluid-structure interaction (FSI) phenomenon, which widely exists in practical engineering (such as flexible risers, bridges and aircraft wings). Conventional numerical simulation and data-driven approaches for VIV analysis often suffer from the challenges of computational cost and dataset acquisition. This paper proposed a physics-informed neural network (PINN) enhanced by transfer learning (TL) to study a VIV system (2D). The TL-PINN only used 1/2, 1/4 and 1/8 of the training set (for PINN model) to reconstruct the information of flow field and structure, but with the same prediction accuracy as PINN model. In addition, a stepwise iterative training strategy was proposed to train PINN model. The strategy can effectively reduce the dependence of neural networks on data sets, so as to reduce the training cost of PINN model. The results show that PINN with the stepwise iterative training strategy and TL-PINN can enhance learning efficiency and keep predictability without requiring a huge quantity of datasets. Based on the proposed method, limited and scattered label data from monitoring, numerical and experimental can be fused to realize the reconstruction and prediction of flow field and structure information. It can break the limitation of monitoring equipment and methods in practical projects, and promote the in-depth study of VIV.

Aeroelastic evaluation of a flexible high aspect ratio wing UAV: Numerical simulation and experimental flight validation

Aeroelastic phenomena occur when aerodynamic and structural loads interact in an unstable manner. This is a major concern in the design of modern high aspect ratio wing (HARW) aircraft, as these structures are more prone to deflection during flight and thus affecting the flight performance. Studying these effects in full scale aircraft is prohibitively costly and risky. Scaled flight test demonstrators with flexible wings offer a low cost and risk solution to evaluate the aeroelastic response of HARW aircraft. In this work, the sizing, design and testing of an in-house developed flexible-wing unmanned air vehicle (UAV) is presented. First, the rigid-wing aircraft platform and its performance characteristics are presented, followed by the wing modifications to make it flexible. The wing flexibility was designed such that coupling of flight-dynamic modes and elastic modes is observed in-flight. A full numerical characterization of the aircraft was first performed, including structural, aerodynamic and aeroelastic computational models. Next, ground vibration and static loading tests were performed to validate the structural models. Flight tests were conducted to obtain data on aircraft dynamics, and strain measurements along the wing spar. An indirect shape reconstruction method based on strains was employed to extract wing displacements from measured strain data. Flight test data was then compared against simulated aeroelastic results. It was observed that for the HARW flexible wing aircraft, a degree of coalescence between the aircraft short period mode and first out-of-plane bending mode of the wing was present. By using different excitation functions applied to the control surfaces, this coalescence was recorded during flight tests and good agreement with the simulated results was observed. The results obtained are an important step in the study of fluid-control-structure interaction issues in HARW aircraft.

適応的空力弾性制御モデルの構築と風洞実験による検証 ---圧力制御と翼端変位推定---

GLA (= Gust Load Alleviation) control is an important active safety technology, especially for light-weight flexible wing aircraft with high aspect ratio wings. This paper proposes an adaptive aero-servo-elastic model for control design, including an aerodynamic load distribution estimator using in-flight partial pressure sensing. The GLA control law obtained based on the proposed model consists of pressure control and mode estimation. In general, aero-servo-elastic models require an wing structure dynamics and an aerodynamic model around the wing. However, it is difficult to construct a generic model in advance for complex unsteady aerodynamic phenomena where turbulence and vibration of flexible wings interact. In this case, the optimality and the reliability of a model-based GLA control is significantly reduced. To minimize the uncertainty of the model for unsteady aerodynamics, this method proposes the use of an autoregressive model with time-varying coefficients, which are updated in real-time based on in-flight pressure sensing. The feasibility of the proposed GLA scheme is demonstrated in terms of both pressure control and wing tip displacement estimation through wind tunnel experiments.

Output constraints vibration control for a flexible aircraft wing with prescribed performance

ABSTRACT This study focuses on the vibration control and boundary output constraint problems for a flexible wing system. The coupled twist-bending dynamic model of the flexible wing contains several partial differential equations (PDEs) and ordinary differential equations (ODEs). With the help of prescribed performance functions, an innovative boundary controller is designed to achieve the vibration suppression without the violation of prescribed performance. The proposed controller can make the boundary deformations converge to an arbitrarily small residual set. In addition, a disturbance observer is proposed to eliminate the adverse effect caused by unknown external disturbances. Then the asymptotic stability of the closed-loop system is justified by the Lyapunov’s direct method. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed control scheme.

Adaptive fault-tolerant boundary vibration control for a flexible aircraft wing against actuator and sensor faults

The vibration control problem was investigated in this study in the presence of unknown loss of actuator effectiveness fault and loss of sensor effectiveness fault in a flexible aircraft wing system. A series of partial differential equations was used as the mathematical model of the wing with unknown boundary disturbances. An adaptive fault-tolerant boundary controller was designed accordingly. All signals of the closed-loop control system are globally uniformly bounded and the controlled state asymptotically converges. Numerical simulations were conducted to validate the proposed control scheme.

Status and development of flexible wing aircraft

The flexible wing aircraft is derived from the flexible wing and can be divided into inflatable and parafoil type according to its wing type. The flexible wing aircraft has a broad application in both aerospace, missiles and unmanned aerial vehicles. This paper summarizes the current status and application prospect of flexible wing aircraft by summarizing the research of domestic and foreign scholars. Finally, this paper is summarized and the outlook for development is presented.

SIMULATED PILOT-IN-THE-LOOP TESTING OF HANDLING QUALITIES OF THE FLEXIBLE WING AIRCRAFT

This article aims to indicate the differences between rigid and flexible wing aircraft flying (FQ) and handling (HQ) qualities. The Simulation Framework for Flexible Aircraft was used to provide a generic cockpit environment and a piloted mathematical model of a bare airframe generic high aspect ratio wing aircraft (GA) model. Three highly qualified test pilots participated in the piloted simulation trials campaign and flew the GA model with both rigid and flexible wing configurations. The results showed a negligible difference for the longitudinal HQs between rigid and flexible wing aircraft. However, significant changes were indicated for the lateral/directional HQs of the flexible wing aircraft. A wing ratcheting phenomenon manifested itself during the roll mode tests, the spiral mode exhibited neutral stability and the Dutch roll mode shape changed from a horizontal to a vertical ellipse. The slalom task flight tests, performed to assess the FQs of the aircraft, revealed the degradation of both the longitudinal and lateral/directional FQs.

The Final Word on Flutter?

A multimillion European research program is developing the tools that will shape the next generation of flexible and efficient aircraft wings

Online model‐free reinforcement learning for the automatic control of a flexible wing aircraft

The control problem of the flexible wing aircraft is challenging due to the prevailing and high nonlinear deformations in the flexible wing system. This urged for new control mechanisms that are robust to the real-time variations in the wing's aerodynamics. An online control mechanism based on a value iteration reinforcement learning process is developed for flexible wing aerial structures. It employs a model-free control policy framework and a guaranteed convergent adaptive learning architecture to solve the system's Bellman optimality equation. A Riccati equation is derived and shown to be equivalent to solving the underlying Bellman equation. The online reinforcement learning solution is implemented using means of an adaptive-critic mechanism. The controller is proven to be asymptotically stable in the Lyapunov sense. It is assessed through computer simulations and its superior performance is demonstrated on two scenarios under different operating conditions.

Parameter Varying Mode Decoupling for LPV systems

The paper presents the design of parameter varying input and output transformations for Linear Parameter Varying systems, which make possible the control of a selected subsystem. In order to achieve the desired decoupling the inputs and outputs of the plant are blended together, and so the MIMO control problem is reduced to a SISO one. The new input of the blended system will only interact with the selected subsystem, while the response of the undesired dynamical part is suppressed in the single output. Decoupling is achieved over the whole parameter range, and no further dynamics are introduced. Linear Matrix Inequality methods form the basis of the proposed approach, where the minimum sensitivity is maximized for the subsystem to be controlled, while the H∞ norm of the subsystem to be decoupled is minimized. The method is evaluated on a flexible wing aircraft model.

Adaptive neural network vibration control of a flexible aircraft wing system with input signal quantization

In this paper, we study a boundary vibration control problem for a flexible aircraft wing system with input signal quantization and unknown input disturbances. The wing system is a distributed parameter system with coupled bending and torsional deformations. The dynamic behavior of the flexible wing is represented by partial differential equations (PDEs) and several ordinary differential equations (ODEs). The control methods that already exist for similar PDE systems, which is suitable for general situations, will reduce the control precision of the system after control input quantization and may generate stability losses in serious cases. To suppress the elastic deformation of the wing after input quantization, an adaptive quantitative control scheme based on a radial basis function (RBF) neural network is proposed. The new controller can restrain the wing vibration deformation successfully and the stability of the closed-loop system is verified via the Lyapunov's direct method. Finally, numerical simulations illustrate the effectiveness and superiority of the control system.

Online model-free controller for flexible wing aircraft: a policy iteration-based reinforcement learning approach

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.

Online Multi-Objective Model-Independent Adaptive Tracking Mechanism for Dynamical Systems

The optimal tracking problem is addressed in the robotics literature by using a variety of robust and adaptive control approaches. However, these schemes are associated with implementation limitations such as applicability in uncertain dynamical environments with complete or partial model-based control structures, complexity and integrity in discrete-time environments, and scalability in complex coupled dynamical systems. An online adaptive learning mechanism is developed to tackle the above limitations and provide a generalized solution platform for a class of tracking control problems. This scheme minimizes the tracking errors and optimizes the overall dynamical behavior using simultaneous linear feedback control strategies. Reinforcement learning approaches based on value iteration processes are adopted to solve the underlying Bellman optimality equations. The resulting control strategies are updated in real time in an interactive manner without requiring any information about the dynamics of the underlying systems. Means of adaptive critics are employed to approximate the optimal solving value functions and the associated control strategies in real time. The proposed adaptive tracking mechanism is illustrated in simulation to control a flexible wing aircraft under uncertain aerodynamic learning environment.

Adaptive fault-tolerant boundary control for a flexible aircraft wing with input constraints

This paper discusses the boundary control problem of actuator input constraints for a flexible aircraft wing in the presence of unknown external disturbances and unknown actuator failure. Based on the application of smooth hyperbolic function and a projection algorithm, an innovative adaptive boundary controller with an external disturbance estimator is designed to suppress the bending and twist deformations of the flexible aircraft wing. The flexible aircraft wing is modeled as a coupled twist-bending system, the dynamics of which are described by several partial differential equations (PDEs) and ordinary differential equations (ODEs). The proposed boundary control scheme is shown to satisfy the input restrictions and physical conditions. The elastic vibration and vibration rate of the flexible aircraft wing system is also eliminated successfully even after the actuator failure. Both theoretical analysis and numerical simulations are provided which demonstrate the effectiveness of the proposed scheme.

Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft

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.

Nonstationary Random Vibration Analysis of Wing with Geometric Nonlinearity Under Correlated Excitation

An algorithm that integrates Karhunen-Loeve expansion (KLE), nonlinear finite element method (NFEM), and a sampling technique to quantify the uncertainty is proposed to carry out random vibration analysis of a structure with geometric nonlinearity under correlated nonstationary random excitations. In KLE, the eigenvalues and eigenfunctions of the autocovariance are obtained by using orthogonal basis functions, and the KLE for correlated random excitations relies on expansions in terms of correlated sets of random variables. The autocovariance functions of excitation are discretized into a series of correlated excitations, and then the structural response is carried out by using NFEM and sampling techniques. The proposed algorithm is applied to both rigid and flexible aircraft wings. Two different types of the boundary condition are studied for the flexible wing: fixed and large mass method (LMM). Results show that the geometric nonlinearity has a stiffening effect on the behavior of the aircraft wing, resulting in an oscillatory response with a lower amplitude, and changes the distribution of the random responses. The response due to LMM boundary condition that is closer to the actual conditions is smaller than the response obtained using fixed boundary condition.

Adaptive fault‐tolerant control for a nonlinear flexible aircraft wing system

AbstractFault‐tolerant control problems have been extensively studied in all kinds of control systems. However, there is little work on fault‐tolerant control for distributed parameter systems. In this paper, a novel adaptive fault‐tolerant boundary control scheme is proposed for a nonlinear flexible aircraft wing system against actuator faults. The whole system is regarded as a distributed parameter system, and the dynamic model of the flexible wing system is described by a set of partial differential equations (PDEs) and ordinary differential equations (ODEs). The proposed controller is designed by using the Lyapunov's direct method and adaptive control strategies. Based on the online estimation of actuator faults, the adaptive controller parameters can update automatically to compensate the actuator faults of the system. Besides, a fault‐tolerant controller is also developed for this system in the presence of external disturbances. Differing from existing works about adaptive fault‐tolerant control, the adaptive controller presented in this paper is designed for a distributed parameter system. Finally, numerical simulations are carried out to illustrate the effectiveness of the proposed control scheme.

Boundary control of flexible aircraft wings for vibration suppression

ABSTRACTIn this paper, we propose a boundary control strategy for vibration suppression of two flexible wings. As a basic approach, Hamilton's principle is used to ascertain the system dynamic model, which includes governing equations – four partial differential equations and boundary conditions – several ordinary differential equations. Considering the coupled bending and torsional deformations of flexible wings, boundary control force and torque act on the fuselage to regulate unexpected deformations of flexible wings. Then, we present the stability analysis of the closed-loop system through Lyapunov's direct method. Simulations are carried out by using finite difference method. The simulation experimental results illustrate the significant effect of the developed control strategies.

Multiobjective optimization of an aircraft wing design with respect to structural and aeroelastic characteristics using neural network metamodel

The multidisciplinary design optimization (MDO) is an important concept that focuses on bringing together disciplines involved with machine design. It is possible to use MDO in any stage of an aircraft design, that is in the conceptual, preliminary, or detailed design, as long as the numerical models are fitted to each of these stages. This work describes the development of a multidisciplinary design optimization applied to flexible aircraft wings, with respect to structural and aeroelastic characteristics. As tools for the aircraft designer, the numerical models must be fairly accurate and fast. Therefore, metamodels for the critical flutter speed prediction of aircraft wings were considered, thereby reducing significantly the computational cost of the optimization. For this purpose, artificial neural networks (NN) metamodeling is evaluated, based on their inherent properties in dealing with complex mappings. The NN metamodel is prepared using an aeroelastic code based on finite-element model coupled with linear potential aerodynamics. Results of the metamodel performance are presented, from where one can note that the NN is well suited for flutter prediction. Multiobjective optimization (MOO) using the genetic algorithm (GA) based on non-dominance approach is considered. The objectives were the maximization of critical flutter speed and minimization of structural mass. One case study is presented to evaluate the performance of the MOO, revealing that overall optimization process actually achieves the search for the Pareto frontier.