Explore the vast range of titles available.

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.

Due to the increase in aircraft systems complexity along the decades and the continuous certification requirement improvements for safer operations, the safety assessment accomplished by systems engineers has been demanding more effort from the specialists to make a complete evaluation of the system and respective interfaces. The capability of predicting the real effects of components failures in the system behavior to make better assessments of their severities and to support troubleshooting processes during aircraft operation has also represented a challenging activity. In that context, the development of computational models and simulation has become a common practice in the industry. Therefore, the aim of the present work was to demonstrate the benefits of working in a cohesive manner with two particular modeling techniques: a physical modeling based computational software and the bond graph concepts, to enhance the specialist’s comprehension about the impacts of particular failures in system performance. As a case study, an aircraft hydraulic brake system has been chosen since it performs important, safety-related functions in aircraft operation. For that purpose, a computational model parameterized in LMS Amesim® software is used, after a deep validation process, to assess the behavior of system relevant variables in normal and faulty operating conditions. In parallel, a bond graph diagram representative of a system component is applied as a support tool to assess typical failure modes and help selection of relevant ones for simulation.

In this work, the authors propose a formation control strategy of a group of three multirotor aerial vehicles being able to avoid multiple obstacles and collisions. To deal with this problem, a decentralized architecture is proposed which has one model predictive controller per vehicle including a set of convex constraints on the vehicle’s position to prevent collisions with other agents and different shapes of obstacles. The resulting decentralized scheme controls the formation based on a virtual structure approach. For the purpose of avoiding collisions, each local controller considers the predicted position of every neighbor vehicles. The effectiveness of the developed scheme is demonstrated through numerical simulations considering a “figure-of-eight” as the reference trajectory, and the results show its capability to handle thrust force, obstacle and collision avoidance constraints.

The motivation to accurately model the dynamics of flexible aircraft grew with the development of energy-efficient aircraft, consequently, great aspect ratio aircraft. The development of an accurate model that represents the flight dynamics of a flexible aircraft has been pursued by industry and aeronautical research organizations during the last decades. One of these approaches is to find a flexible aircraft model using systems identification methods. This research aims to apply an integrated model containing longitudinal and lateral directional rigid body dynamics, coupled to the first four flexible body modes, for identification and validation from flight test data. The Unmanned Aerial Vehicle (UAV) Eolo with the flexible wing is used during the experiments. Initially, a finite element structural model (FEM) based on beam elements, concentrated masses, and rigid bars was used. The quasi-stationary panel model based on the Vortex Lattice Method (VLM) was adopted for the aerodynamic model. Two diagonal matrices were used to correct the aerodynamic influence coefficients (AIC) matrix obtained via VLM before and post-multiplication for aircraft identification. The estimation of the main diagonal elements of each matrix was obtained through the Output Error Method in the time domain. A model validation analysis was carried out, which shows a good correlation between the model and measurement data. In conclusion, getting correction matrices instead of stability derivatives is beneficial because matrices can be used more directly during the aeronautical design and observe the behavior concerning loads.

This work presents a strategy to control nonlinear responses of aeroelastic systems with controlsurface freeplay. The proposed methodology is developed for the three degrees of freedom typicalsection airfoil considering aerodynamic forces from Theodorsen’s theory. The mathematical modelis written in the state space representation using rational function approximation to write theaerodynamic forces in time domain. The control system is designed using the fuzzy Takagi-Sugeno modeling to compute a feedback control gain. It useds Lyapunov’s stability function and linear matrix inequalities (LMIs) to solve a convex optimization problem. Time simulationswith different initial conditions are performed using a modified Runge-Kutta algorithm to comparethe system with and without control forces. It is shown that this approach can compute linearcontrol gain able to stabilize aeroelastic systems with discontinuous nonlinearities.

The acoustic design of cavities is an important task in a variety of engineering applications, from automotive or aerospace industries to equipment coating designs. In this work, the acoustic impedance functions (a frequency domain model) were calculated using analytical, numerical, and experimental methods. Those different approaches were presented in a unified manner in order to allow comparisons among them. The relationship of the impedance function and a classical frequency response function (FRF) was also established. A circular duct of rigid walls was assumed with different boundary conditions as closed end, as well as opened and absorbed extremities. Three duct configurations were implemented in order to compare analytical, numerical, and experimental results. Finally, it could be possible to evaluate some aspects that are characteristic of a large range of acoustic systems applications as the existence of complex modes and frequency-dependent behavior of absorption material. This study aims the usage of the impedance functions to analyze the acoustic behavior of cavities, as well as to compose the background in order to develop, in the future, an acoustic modeling process using impedance functions.

This article focuses on the problem of parameter estimation of the uncoupled, linear, short-period aerodynamic derivatives of a “Twin Squirrel” helicopter in level flight and constant speed. A flight test campaign is described with respect to maneuver specification, flight test instrumentation, and experimental data collection used to estimate the aerodynamic derivatives. The identification problem is solved in the time domain using the output-error approach, with a combination of Genetic Algorithm (GA) and Levenberg-Marquardt optimization algorithms. The advantages of this hybrid GA and gradient-search methodology in helicopter system identification are discussed.

This paper treats the problem of position formation flight control of a group of three multirotor aerial vehicles under obstacle and collision avoidance constraints. In order to solve the problem, a distributed architecture with model predictive controllers for each vehicle includes a set of convex constraints on the vehicles’s position to prevent collisions with other vehicles and obstacles. The resulting distributed scheme controls the formation based on a virtual structure approach where the computers of the architecture exchange position data through diagrams in Simulink. The performance of the method is assessed through simulations considering that the vehicles are subject to disturbance forces and the results show the effectiveness and the ability of the control architecture to handle the obstacle and collision avoidance constraints.

Most of the established procedures for analysis of aeroelastic flutter in the development of aircraft are based on frequency domain methods. Proposing new methodologies in this field is always a challenge, because the new methods need to be validated by many experimental procedures. With the interest for new flight control systems and nonlinear behavior of aeroelastic structures, other strategies may be necessary to complete the analysis of such systems. If the aeroelastic model can be written in time domain, using state-space formulation, for instance, then many of the tools used in stability analysis of dynamic systems may be used to help providing an insight into the aeroelastic phenomenon. In this respect, this paper presents a discussion on the use of Gramian matrices to determine conditions of aeroelastic flutter. The main goal of this work is to introduce how observability gramian matrix can be used to identify the system instability. To explain the approach, the theory is outlined and simulations are carried out on two benchmark problems. Results are compared with classical methods to validate the approach and a reduction of computational time is obtained for the second example.

Strain gauges and strain measurements have been widely used in structural health monitoring (SHM) systems as a means of detecting and localizing damage, due to their higher sensitivity to local damage. These damage identification techniques normally use strain related measurements such as the mode curvature, strain frequency response function or strain energy as the main parameter to detect damage. However, damage detection techniques based on acceleration measurements have also been investigated in the past, using modal parameter comparison and other methodologies. In this paper, the use of vibration-based strain measurements for use in SHM systems will be evaluated, with the purpose of characterizing their higher sensitivity in damage detection, when compared to other vibration measurements, such as acceleration-based measurements. Since the choice and use of the most damage sensitive parameter can lead to a more sensitive and robust system, the assessment of the more suitable sensor and processing of information is very important. For this purpose, numerical and experimental examples will be discussed to evaluate the higher performance of the strain gauges.