Flight Dynamics Research Articles (Page 1)

To address the issue that traditional snake optimization (SO) algorithms tend to get trapped in local optima when identifying aerodynamic parameters of high-spin projectiles—where complex flight dynamics and measurement noise further complicate the process—this paper proposes an enhanced snake optimization algorithm integrated with genetic algorithm (GA) mechanisms. Specifically, the improved algorithm incorporates GA-based selection and crossover operations into the SO framework, aiming to strengthen global search capability by simulating not only snakes’ natural foraging and combat behaviors but also the evolutionary characteristics of genetic algorithms. For handling noisy trajectory data, Kalman filtering is applied to denoise measured information, laying a reliable foundation for subsequent parameter identification. The method utilizes segmented trajectory data of high-spin projectiles across different speed stages for analysis. Comparative experiments with the traditional SO algorithm and other optimized variants demonstrate that the proposed approach reduces identification errors by 49%, significantly outperforming conventional methods in accuracy. Further validation with full-trajectory measured data shows that when the identified aerodynamic parameters are substituted into ballistic equations, the deviation between calculated and actual impact point coordinates is minimal, confirming their effectiveness. Notably, the improved algorithm does not rely on precise initial parameter settings, enhancing its adaptability in practical scenarios. In summary, it provides a robust solution for accurately identifying projectile aerodynamic parameters and holds promise for engineering applications.

Large-scale data analysis has been the subject of numerous studies recently. In many applications of today's data-intensive world, data is typically brought in continually as data streams. Analytics engines that handle streaming data must be able to react to data that is in motion. Data streams provide special challenges because traditional methods for data mining and machine learning are meant for static information. They are less suited to consider the representative characteristics of data streams and are very less suitable to effectively analyse data that is growing quickly. The authors through this research viz. A-MERIT-C - a dynamic learning multitiered ensemble-based flight real time data analysis system. Through this research authors have presented an active learning dynamic real time data stream analysis model built with self-tuning ensemble learning framework, able to quickly adapt to concepts in near real time streaming data analysis. The conceptual architectural framework illustrated through this research is adaptive to deal with the dynamics related with real time data through the evolving classifier pool (i.e. best performing classifiers get added to classifier pool at every epoch). One more distinguishing characteristic of -A-MERIT-C is instead of using traditional hold out evaluation, it uses prequentially evaluated classifiers. A-MERIT-C's unique features provide significant gains in accuracy, precision, and AUC for streaming data analytics; however, it can also overcome the drawbacks of current algorithms, including concept evolution and feature drift, by using incremental learning and feedback.

With the rapid advancement of UAV, image matching algorithms for visual navigation have been increasingly widely used. However, extensive research demonstrate that existing algorithms exhibit insufficient matching accuracy and long response time in challenging aerial scenes, which cannot meet the accuracy requirements of UAV visual navigation. In this paper, we propose an efficient image matching method for UAV visual navigation, named as DALGlue, based on convolutional neural network feature extraction algorithm and feature matching network with linear attention mechanism. DALGlue uses dual-tree complex wavelet transform to preprocess the collected aerial images, which enhances structural information and fine details. Compared with directly processing raw images, dual-tree complex wavelet transform module solves the problem of edge blurring in UAV dynamic flight. Then, an adaptive spatial feature fusion module is developed to extract features from images and calculate feature points and descriptors. In addition, we employ linear attention mechanism to aggregate image features, which can effectively reduce computational costs while improving network characteristics. Finally, the Sinkhorn algorithm is used to calculate the allocation matrix and output optimal assignment. DALGlue demonstrates a unique balance between accuracy and real-time performance, which can be operate under strict computational and memory constraints. In comparison to the state-of-the-art method LightGlue, the experimental results show that DALGlue obtains 11.8% points improvement in MMA. On the MegaDepth-1500 benchmark, DALGlue achieves the AUC@5 °/10 °/20 ° values of 57.01, 73.00, and 84.11 respectively, which effectively improved match precision.

This paper addresses the issue of existing research that fails adequately capture the spatiotemporal nonstationarity caused by the building of occlusion and flight dynamics in air-to-ground channels from unmanned aerial vehicles (UAVs) in urban scenarios. This study focuses on the angular-altitude correlations of three key metrics: path loss (PL), shadow fading, and the Ricean K-factor. A dynamic path-loss model incorporating the look-down angle is proposed, an exponential decay model for the shadow-fading standard deviation is constructed, and a model for the angle-dependent variation of the Ricean K-factor is established based on line-of-sight probability. Simulations were conducted in two urban-geometry scenarios using WinProp to evaluate the combined effects of flight altitude and elevation angle. The results indicate that path loss decreases and subsequently stabilizes with increasing elevation angle, the shadow-fading standard deviation decreases significantly, and the Ricean K-factor increases with angle and saturates at high angles, in agreement with theoretical predictions. These models are more adaptable to UAV mobility scenarios than traditional fixed exponential models and provide a useful basis for UAV link planning and system optimization in urban environments.

Abstract Flying-wing aircraft with high-aspect ratios have received extensive attention due to their outstanding aerodynamic efficiency and stealth capabilities. This type of aircraft, however, may suffer from rigid-elastic coupling flutters, such as a body-freedom flutter, owing to the interaction among flight dynamics, structural dynamics and aerodynamics. This paper surveys the advances in modeling and analysis methods, control strategies and experimental validations related to those flutters and their active suppressions. The paper begins with the modeling approaches in different frames of reference for a rigid-elastic coupling aero-servo-elastic system to emphasize their roles and merits in describing rigid-elastic interactions. Then, it discusses the mechanism of a rigid-elastic coupling flutter, accounting for the coupling of flight dynamics and aeroelastic vibrations. Afterwards, the paper presents a comparison among the control performances of typical active flutter suppression strategies to evaluate the capacity of enhancing aircraft stability and increasing flutter speed. The paper also reviews the wind-tunnel tests and flight tests to verify the active flutter suppression techniques. Unlike other tests, the flight tests of the Aeroelastic Flight Demonstrator (AFD) made by the authors indicate that the active controller could successfully remove the rigid-elastic coupling flutter and greatly increase the flutter speed till the occurrence of a bending-torsion flutter of higher order. Finally, the paper outlines future studies on flying-wing aircraft and active flutter suppression techniques.

For field-deployed sensors, it is important to measure the modulation transfer function (MTF) under actual operational conditions, especially when the sensor is mounted on a moving platform such as an unmanned aerial vehicle (UAV). Mechanical vibrations, flight dynamics, and linear motion along the flight path can degrade system performance and cause image blur. Different UAV types—such as multirotor, vertical takeoff and landing (VTOL), and fixed-wing platforms—exhibit varying levels of motion blur due to their operating characteristics. These platforms typically operate at high speeds, making linear motion blur a primary limiting factor in performance. Direct measurement of the component MTFs corresponding to the three main sources of motion blur is not feasible for these platforms. Therefore, understanding the impact of platform-induced degradation on resolution performance is crucial when designing sensor systems. Predicting the MTF from platform-based measurements, such as those from an inertial navigation system (INS), has been a long-standing challenge. Previous efforts to estimate the component MTFs for each type of motion have shown significant errors compared to measured results. In this study, low-frequency motion is incorporated into the linear motion component MTF rather than treated as a separate component. This approach yields a close match between estimated and measured system MTFs, significantly reducing prediction error.

Flight controls are experiencing a major shift with the integration of reinforcement learning (RL). Recent studies have demonstrated the potential of RL to deliver robust and precise control across diverse applications, including the flight control of fixed-wing unmanned aerial vehicles (UAVs). However, a critical gap persists in the rigorous evaluation and comparative analysis of leading continuous-space RL algorithms. This paper aims to provide a comparative analysis of RL-driven flight control systems for fixed-wing UAVs in dynamic and uncertain environments. Five prominent RL algorithms that include Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO) and Soft Actor-Critic (SAC) are evaluated to determine their suitability for complex UAV flight dynamics, while highlighting their relative strengths and limitations. All the RL agents are trained in a same high fidelity simulation environment to control pitch, roll and heading of the UAV under varying flight conditions. The results demonstrate that RL algorithms outperformed the classical PID controllers in terms of stability, responsiveness and robustness, especially during environmental disturbances such as wind gusts. The comparative analysis reveals that the SAC algorithm achieves convergence in 400 episodes and maintains a steady-state error below 3%, offering the best trade-off among the evaluated RL algorithms. This analysis aims to provide valuable insight for the selection of suitable RL algorithm and their practical integration into modern UAV control systems.

The P300 evoked potential, recorded via electroencephalography, serves as a relevant marker of attentional allocation and cognitive workload. This work extracts and analyzes event-related potentials that reflect variations in the cognitive state of military pilots during a complex simulated flight scenario coupled with simultaneous mental arithmetic tasks. The experiment was conducted at the Academia General del Aire (Spain) with 14 military pilots using a high-fidelity flight simulator. The experimental protocol involved dynamic flight instructions combined with arithmetic tasks designed to elicit varying cognitive loads. The results revealed a significant decrease in P300 amplitude across successive sessions, indicating a progressive reduction in attentional engagement due to task habituation and increased cognitive automaticity. Concurrently, P300 latency for correct responses decreased significantly, demonstrating enhanced efficiency in cognitive stimulus evaluation over repeated exposure. However, incorrect responses failed to yield robust results due to an insufficient number of trials. These findings validate the use of P300 as an objective indicator of cognitive workload variations in realistic aviation contexts.

The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency.

This article presents an in-depth flight dynamics analysis of a quadrotor biplane tailsitter and proposes novel dynamic inversion (DI) flight control laws for autonomous hover-to-cruise transition. As the basis for the synthesis and demonstration of such control laws, a flight dynamics model is developed that also accounts for rotor dynamics and rotor-on-wing interactions. The flight dynamics model is trimmed and linearized at discrete increments in flight speed, from hover to cruise flight. The order of the linearized models is reduced by means of residualization, a subset of singular perturbation theory, to enable stability analysis and control design. The stability and response properties are analyzed both at hover and in cruise flight in terms of eigenvalues, motion modes, and frequency responses. A multiloop DI control law is developed, where an outer velocity loop tracks commanded longitudinal, lateral, and vertical ground velocities in the heading frame and computes the desired pitch and roll attitudes for the inner loop to follow. The inner attitude loop ensures stability, disturbance rejection, and appropriate dynamic response about the roll, pitch, and yaw axes. To demonstrate the proposed control strategy and investigate performance limits, two distinct trim and closed-loop transition trajectories are considered: one in which the vehicle performs the transition with a vertical climb component, and another in which it performs a level, forward-only translation. Closed-loop simulations based on the full nonlinear dynamics are used to demonstrate autonomous hover-to-cruise transitions and to assess the minimum feasible transition time before the onset of rotor stall.

Integrated analysis of electric aircraft performance and battery design through coupled modeling of flight and battery dynamics

Inverse estimation of the flight dynamics of a hypersonic aircraft prototype via shock wave measurements

Dynamic Flight Challenges in PEMFC-powered UAVs: Towards Intelligent Management and Sustainable Propulsion

The natural-laminar-flow (NLF) technology is a highly promising method to increase the aerodynamic performance of next-generation commercial transport aircraft, while simultaneously reducing their fuel consumption and CO2 emissions. For an optimal design, detailed knowledge on the boundary-layer evolution over the wings during different steady and dynamic flight conditions is crucial. Temperature-sensitive paint (TSP) is a well-suited optical measurement technique to obtain quantitative information on the spanwise-distributed transition location in model tests at cryogenic temperatures and elevated pressures. For precise boundary-layer transition detection, TSP requires an artificial enhancement of the temperature difference between the laminar and turbulent sections of the boundary layer. This paper presents TSP measurements on a forward-swept wing fuselage-belly fairing configuration, conducted in the European Transonic Windtunnel (ETW), for laminar–turbulent transition detection on the suction side of the wing at various static pitch angles and flight-relevant flow conditions. Three different methods to achieve the necessary temperature difference between the TSP-coated model surface and the flow were tested, and their results were evaluated for their applicability and efficiency in cryogenic testing with TSP. With each method, the transition pattern and its dependency on the pitch angle could be captured successfully with close agreement of the corresponding results.

Abstract Background Digital image processing has become an essential tool for species identification and surface characterization. Among advanced morphological analyses, Minkowski functionals (MFs), a mathematical descriptor derived from integral geometry, offers a quantitative approach to assessing surface features. This study focuses on using scanning electron microscopy (SEM) combined with MFs to analyze the wing morphology of Anopheles mosquitoes, which are significant vectors of malaria. Understanding fine-scale wing morphology is critical for improving species identification and developing effective disease control strategies. Results SEM analysis revealed morphological differences between the two species on both dorsal and ventral wing surfaces. It was observed that there was the presence of scales along the veins and edges of the wings and as well as long hairs distributed across the wing area. High-magnification images enabled detailed analysis of nanometric structures. Quantitative analysis using MFs indicated that An. aquasalis wings presented more pronounced surface elevations, greater height variation, and a higher density of peaks and valleys, while An. darlingi exhibited smoother and more uniform surfaces and presence of nanostructures with the presence of nanostructures. These functional analyses provided a comprehensive understanding of the differences in surface roughness and structural connectivity between the two species. Conclusions The combination of SEM and MFs proved effective for distinguishing mosquito species based on wing surface architecture. This high-resolution, quantitative approach serves as a valuable complement to identification, enabling precise species distinction, and enhancing the understanding of the morphological characteristics that influence flight dynamics, adaptation, dispersion and vector capacity of mosquitoes, contributing to better disease control strategies and potential applications in various biotechnical fields. The application of MFs in conjunction with SEM provides a robust method for quantifying complex surface morphology and can be expanded to other entomological and biological studies.

ABSTRACTQuadrotor unmanned aerial vehicles (UAVs) are increasingly becoming essential tools in applications such as surveillance, military operations, crop monitoring, search and rescue, and inspection of hazardous terrain. Their control is not an easy endeavor due to the underactuated and highly coupled dynamics. Among many control methodologies, sliding mode control (SMC) has long been recognized as one that is insensitive to system nonlinearities and external disturbances. Yet, the inherent chattering effect of SMC will lead to system degradation and actuator damage. To mitigate this limitation, this study proposes an adaptive neuro‐fuzzy inference system‐based sliding mode control (ANFIS‐SMC) method that incorporates the strength of ANFIS and the robustness of SMC to enhance quadrotor trajectory tracking with reduced chattering effects. The control system comprises position, altitude, and attitude controllers that online learn from system errors and control signals and ensure stable and precise flight under dynamic flight conditions. The performance of the ANFIS‐SMC controller developed in the current study is validated using MATLAB/SIMULINK simulations and compared with a classical SMC scheme. Results confirm that a Comparison between SMC and the proposed ANFIS‐SMC controller is conducted in terms of both disturbance and parameter variation, and the proposed ANFIS‐SMC controller has shown better performance improvement of 58.1%. Reduces chattering and achieves improved tracking accuracy, confirming its worthiness for robust quadrotor control tasks.

Application of singular perturbation theory to space flight dynamics problems

Purpose The rapid expansion of urban air mobility demands advanced passive safety systems specifically designed for vertical take-off and landing (VTOL) aircraft. Traditional parachute recovery systems, effective for fixed-wing aircraft, face significant challenges when adapted to a VTOL due to their unique flight dynamics. This study aims to establish methods for analyzing parachute aerodynamic properties and inflation behavior, providing critical insights to optimize parachute recovery systems for VTOL aircraft and enhance their safety and reliability. Design/methodology/approach This paper uses fluid–structure interaction (FSI) simulations using ANSYS LS-DYNA with an incompressible computational fluid dynamics (ICFD) solver and an implicit structural solver in a two-way strong coupling. A detailed infinite mass analysis workflow predicts parachute inflation under constant descent velocities. Canopy and suspension lines are modeled with realistic material properties to accurately simulate dynamic interactions and deployment behavior. Findings This paper demonstrated that the use of LS-DYNA FSI analysis can accurately predict parachute inflation from semi-inflated geometry. The geometry used for simulation was based on a parachute prototype developed at the Aerospace Institute, BUT FME. The simulation results showed a strong agreement with experimental testing, particularly in terms of the drag coefficient and inflated shape. Originality/value This paper verifies the capabilities and accuracy of FSI analysis using LS-DYNA ICFD solver for parachute inflation.

This paper addresses the issue of identifying measurement errors in the MEMS gyroscope, which serves as the primary source of data for the rocket’s inertial navigation system (INS). The research focused on error analysis through static and dynamic testing, followed by a detailed analysis of angular velocity measurement data from the flight of a stabilized rocket, guided to a specific point in space. The objective of the study was to determine and filter gyroscope measurement errors, such as bias, random walk, and noise. An adaptive filter was proposed, which adjusts to the changing dynamics of the rocket, allowing for more effective compensation of these errors. In the final section, conclusions are presented that identified shortcomings in the algorithm and outlined directions for further work on its optimization. The algorithm was validated in static, dynamic, and actual rocket flight conditions.

Atmospheric turbulence, a common phenomenon encountered in aviation, can significantly impact the comfort and safety of the flight crew and the aircraft. This article examines the causes of turbulence, its impact on flight dynamics, and the current strategies used to minimize its effects. We discuss different types of turbulence, their occurrence patterns, and the technologies used in detecting and managing turbulent conditions. By understanding these factors, the aviation industry aims to enhance flight safety and flight crew comfort.