Search Results (341)

This paper presents the development of two adaptive autopilots for the Cessna Citation X business jet aircraft. The two adaptive control strategies, including a dynamic inversion controller and a neural network controller, provide dual adaptation. The control objective consists of tracking the vertical speed, altitude, and heading commands. Dynamic inversion is applied on each output variable, and then the neural network (NN) controller is updated using adaptive law, derived from backpropagation. Dynamic inversion (DI) is achieved locally using a Recursive Least Squares (RLS) algorithm for state estimation. An inner control loop for the pitch, roll and yaw rates is integrated within the autopilots. The longitudinal states were separated from the lateral states in order to differentiate between longitudinal and lateral control. Robustness tests were conducted under turbulence and wind-gust conditions. The autopilot results were compared with flight simulation data from a Cessna Citation X research flight simulator. Results have shown that the autopilots accurately track the vertical speed, altitude and heading reference signals. The flight simulation comparison has shown that the proposed adaptive controllers were better than the one currently on board the Cessna Citation X. Full article

Since the inception of utility-scale wind turbines, there has been a continual increase in the size of the devices used. One drawback of turbine size increase is that the weight of the rotor blades has grown dramatically. Technological advancements have allowed for the creation of light blades to overcome this issue. These lighter rotors are also less stiff than their predecessors and prone to experiencing aeroelastic vibrations that can lead to fatigue damage. Aerodynamic damping occurring during blade vibration has the potential to mitigate those oscillations; thus, understanding its underlying physics provides an extremely useful tool for future blade design. In a series of previous publications, the authors presented a novel reduced-order characterization technique for the oscillatory response of wind turbines, which allows for the analysis of rotor vibrations when excited by wind gust pulses. In this paper, the authors will apply the same gust pulse technique to analyze the physics of blade’s aerodynamic damping, identifying two physical mechanisms. The first acts either as a damper, or as an energy feeder, depending on operational conditions. The second operates in a purely dissipative manner. Results of numerical experiments on several operational scenarios illustrating these behavioral responses will be presented and discussed. Full article

Dragonflies exhibit remarkable flight stability in unsteady environments, largely due to aerodynamic interaction between their forewings and hindwings. This study investigates gust response in dragonfly-inspired micro-aerial vehicles (MAVs) from a system dynamics perspective, with emphasis on the aerodynamic role of tandem-wing interaction rather than control compensation. A six-degree-of-freedom (6DOF) rigid-body framework is developed and coupled with a quasi-steady aerodynamic model that includes explicit phase-dependent interaction between forewing and hindwing forces. Gusts are introduced as time-varying inflow perturbations, allowing physically consistent analysis of how disturbances propagate through aerodynamic loading into vehicle motion. Simulations are performed for representative flight conditions, including gliding, hovering, and gust-perturbed ascent. The results show bounded trajectory, velocity, and attitude responses under sustained gust excitation, even with conservative baseline control. Force and energy analyses indicate that wing–wake interaction redistributes aerodynamic loads in time and reduces peak force and moment fluctuations before they reach the rigid-body dynamics. This behavior is interpreted as passive aerodynamic filtering of gust disturbances inherent to the tandem-wing configuration. Comparative simulations using backstepping control and Active Disturbance Rejection Control (ADRC) further show that the dominant gust attenuation arises from aerodynamic configuration rather than from control action. Although the aerodynamic model is quasi-steady, the framework reproduces key trends reported in biological and CFD-based studies and provides a numerical foundation for future wind-tunnel and free-flight experiments on configuration-level gust attenuation. Full article

This paper focuses on the modeling and design of an adaptive vortex generator (AVG). The device is actuated through shape memory alloy (SMA) elements. The interest of the research community in these devices is due to their ability to improve the performance of the aircraft, directly altering and controlling the boundary layer. Their action consists of energizing the flow, thereby hindering separation. The peculiarity of the presented AVG architecture lies in its compactness and adaptability, which allows for its activation just for some specific phases that are not adequately covered by the conventional. This system can enable load alleviation in the cruise phase when a gust occurs (spoiler modality) and stall prevention in high-lift conditions (vane modality). These two working capabilities can be obtained by mounting the AVGs at different angles of incidence, with respect to the direction of the flow. The present paper is structured as follows. First, the project of RADAR, hosting the activities, is presented with specific focus on the main objectives and on the strategy of maturation of the technologies. Then, attention is paid to the simulations of the aerodynamic field produced by the AVG. These outcomes have driven the next part of the work, focusing on the identification of the architecture of the AVG. A dedicated finite element modeling approach was implemented to address the design task, even in the presence of SMA non-linear elements. Three main operational phases were simulated: (1) the stretching of the springs up to their connection to the architecture (pre-load phase); (2) the elastic recovery of the springs and the achievement of equilibrium with the hosting structure; and (3) the activation of the springs through heating to deflect the AVG. The simulations proved the capability of the system to produce the required deflection/deployment, even under the most severe load conditions. In particular, the simulations highlighted the capability of the system to produce a deflection of the vortex generator of 83.5 deg under the most severe load conditions, against the required value of 80 deg. This result was obtained by also keeping the structural safety factor at a value of four, in line with the wind tunnel facility requirement. Another key outcome of the dynamic analysis was the absence of coupling with vortex shedding, since the system resonance frequencies (135 and 415 Hz) are well outside the vortex-shedding frequency range (500–1400 Hz). Full article

Owing to its large flexible envelope, an airship is highly sensitive to environmental disturbances, such as wind gusts. Fluid–structure interaction induces structural deformation, which modifies the aerodynamic force distribution and introduces additional coupling effects. Furthermore, servo-elastic deformation alters the position and orientation of actuators mounted on the envelope, resulting in deviations between commanded and actual control forces. To address these issues, a composite control strategy integrating trajectory tracking and active elastic deformation suppression is proposed for a flexible airship equipped with multiple vectored propellers. Structural flexibility is explicitly incorporated into the dynamic model through modal decomposition, where the generalized coordinates and their time derivatives associated with deformation modes are included in the system state vector. A disturbance observer is developed to estimate actuator-level force deviations induced by elastic deformation, and the estimated disturbances are compensated in real time. Based on this formulation, a composite control framework, referred to as servo-elastic control, is established. The framework consists of a trajectory tracking controller and a displacement compensation module to achieve simultaneous motion regulation and structural deflection suppression. Numerical results demonstrate that the displacement at vectored thrust actuator attachment points is reduced to approximately 10% of that obtained using a trajectory tracking controller alone. The proposed method achieves significant deformation suppression without degrading position tracking performance, thereby enhancing control effectiveness and system stability of flexible airships. Full article

Accurate reproduction of deterministic gusts in wind tunnels is essential for studying unsteady aerodynamics and aeroelastic response in aircraft, uninhabited aerial vehicles, and wind turbines. This work presents the design, experimental characterization, and numerical modelling of a low-speed gust generator based on oscillating vanes, capable of producing high-amplitude gusts in strongly unsteady flow regimes. Cross-flow hot-wire measurements are combined with time-accurate computational fluid dynamics simulations to analyze gust formation and propagation. Classical ‘1-cos’ gusts are shown to exhibit pronounced negative velocity peaks associated with start–stop vortex shedding. A modified vane motion protocol is proposed that significantly reduces the negative peak factor while preserving a substantial gust ratio over a wide range of reduced frequencies. Measurements are supplemented with computational fluid dynamics (CFD) simulations. The CFD study included 2D and 3D URANS as well as higher fidelity DES simulations. Flow-field analysis reveals that secondary variations in gust angle arise from nonlinear interactions between vortices shed by adjacent vanes and are influenced by wind-tunnel confinement. The results provide physical insight into the limitations of oscillating-vane gust generators and guidance for the design of high-fidelity gust-generation systems. Full article

A crucial problem for autonomous aerial operations is to provide dependable and strong control of unmanned aerial vehicles (UAVs) in adverse environmental circumstances. The current paper provides an extensive analysis of the vehicle dynamics and control of drones in strong wind fields with altitude-dependent wind shear, wind gusts, and turbulence. A comparative evaluation of sliding mode control (SMC), linear quadratic regulator (LQR), model predictive control (MPC), adaptive constrained adaptive linear control (ACALC), and higher-order control barrier function (HOCBF)-based control in the context of trajectory tracking performance, control effort, and robustness is carried out. Simulation outcomes show that SMC exhibits superior robustness to sudden wind disturbances and the most consistent tracking accuracy under stochastic variations; HOCBF and ACALC provide comparable high precision with added constraint enforcement and adaptive capability, respectively; MPC has smooth control and minimal energy consumption; and LQR has a high level of computational efficiency with significantly tolerable tracking performance. Monte Carlo calculations are conducted to measure tracking errors and control energy under the stochastic wind variations, and the capability of the proposed control strategies to remain resilient in uncertain conditions is brought to light. The results provide useful information about the architecture of effective controllers used in UAVs during severe weather conditions and underline the compromises between the accuracy of tracking, the control effort, and the energy consumption. The suggested framework offers an effective and scalable system suitable for reliable autonomous drone activity in complicated reality settings. Full article

The 2025 fire season in Portugal was marked by large fires, underscoring the vulnerability of the forested areas to fire. The study analyzes the main meteorological conditions during a critical period of fire activity and addresses the following question: Why can the northeast (NE) weather pattern be so critical for fire danger in Portugal? Fire severity in the Arouca wildfire, the largest fire of the period, was estimated using a methodology that integrates foundation vision models with computer vision algorithms. ECMWF analyses and convection-permitting Meso-NH simulations are used to examine large-scale circulation and the mesoscale environment, respectively. Synoptic-scale analysis revealed the Azores anticyclone centered slightly northwest of the Iberian Peninsula (IP), with its eastern sector directly affecting the northern IP under north/northeast winds. The hectometric-scale simulation demonstrated that orographically enhanced wind gusts over the northern Portuguese mountains substantially intensified near-surface fire-weather conditions when the winds were nearly easterly. Furthermore, strong low-level winds and atmospheric stability constrained vertical plume growth, favoring horizontal smoke transport. In addition, the study highlights that Arouca’s fire had 88% of its area affected with moderate to high severity. Overall, the results demonstrate that the interaction between large-scale NE circulation and local orography plays a decisive role in amplifying fire danger in northern Portugal, emphasizing the need for high-resolution atmospheric modeling to identify fire-prone regions under specific synoptic patterns. Full article

Wind gusts pose an increasing threat to sustainable development, damaging resilient infrastructure (SDG 9), disrupting clean energy systems (SDG 7), and endangering community safety (SDG 11). However, the reliability of early warning systems remains limited by systematic biases in Numerical Weather Prediction (NWP) models and insufficient uncertainty quantification, particularly in regions with sparse monitoring networks. To address these challenges in sustainable disaster risk reduction, this study proposes a physics-informed deep learning framework—the Physics-Informed Spatial Attention Network (PISA-Net). The model integrates high-resolution WRF-UPP forecasts as a physical prior within a Transformer-based architecture, enabling effective bias correction and spatial dependency learning under data-sparse conditions. A hybrid probabilistic learning objective is employed to simultaneously improve deterministic gust predictions and provide calibrated uncertainty estimates. Evaluated on 61 extratropical cyclone events in the northeastern United States, PISA-Net substantially outperforms baseline NWP and conventional deep learning models, reducing the mean absolute error and root mean square error to 1.75 m/s and 2.26 m/s, respectively. In addition, the resulting 95% prediction intervals are well calibrated and offer reliable risk-based guidance. By improving both the accuracy and credibility of wind gust forecasts, PISA-Net provides a practical decision-support tool for infrastructure maintenance, wind farm operations, and public safety planning. This work demonstrates the potential of physics-informed deep learning to strengthen sustainable early warning systems in data-sparse regions. Full article

Severe linear convective windstorms (SLCWs) account for 66% of the total windthrow area in Northern Eurasian forests. However, in many cases, these events do not result in forest damage. The aim of this study is to reveal the features of storms that determine whether or not they cause forest damage. The study examines the relationship between windthrow occurrence and the characteristics of SLCW (seasonality, wind gusts and accompanying rainfall), as well as their formation environments. The sample includes 351 SLCW events that occurred in Northern Eurasian forests between 1986 and 2024. These are subdivided into two subsamples: 181 SLCW events with wind gusts of ≥25 m s⁻¹, but without substantial damage to forests (SR_ND), and 170 SLCW events associated with windthrow (SR_WD). Since the subsamples are similar in terms of forest stand characteristics, their differences are likely linked to differences in the characteristics of the SLCWs themselves. In general, SR_WD events are accompanied by stronger wind gusts and rainfall than SR_ND events. The difference in rainfall amounts is more pronounced in the Integrated Multi-satellitE Retrievals for Global Precipitation Monitoring (GPM IMERG) satellite-derived data than in the data from weather stations. Springtime events contribute substantially more to SR_ND (26%) than to SR_WD (12%). According to the ERA5 reanalysis, SR_WD events form in conditions of greater thermodynamic instability and stronger wind shear than SR_ND events, i.e., under conditions that are generally more favorable for more severe windstorms. Obtained results can be further used to assess probable changes in forest damage caused by SLCW events based on projections of rainfall intensity and convective variables in a changing climate. Full article

Interest in wind energy systems of different power ratings has increased significantly in recent years; however, low-power wind turbines are particularly sensitive to wind gust disturbances, which strongly affect their power electronic systems. In this work, a control strategy is proposed for regulating the output voltage of a buck converter integrated into a small wind turbine. To this end, a symmetric variable gain is incorporated into the classical sliding mode control framework, enabling the controller to dynamically adjust the control effort according to the operating conditions. The main objective of the proposed approach is to mitigate output voltage fluctuations induced by Extreme Operating Gusts (EOGs), which have a more pronounced impact on low-power wind turbines. The effectiveness of the proposed controller is validated through both simulation and experimental results. Full article

Knowledge of the maximum gust expected over a period of years is essential for offshore structures design. Because long records of gust speed are not normally available, maximum gusts have traditionally been estimated by multiplying the maximum expected hourly or 10 min wind speed by a gust factor. That calculation ignores the possibility that the highest gust might not occur in the hour with the highest mean wind speed. A similar problem arises in the estimation of the maximum expected individual wave height. By analogy with the accepted method of calculating maximum wave heights, we demonstrate how maximum gusts can be calculated from time series of average wind speed and wind gust distributions. We used measurements from the IJmuiden meteorological mast offshore from The Netherlands to find wind gust distributions. The IJmuiden data is particularly useful for studying gusts because four years of measurements were made at a sampling frequency of 4 Hz. Those distributions were used to predict extreme values of gusts in a storm using methods similar to those used in wave height calculations. The resulting extreme values closely matched extreme values calculated directly from the measured maximum gusts in each storm. The methods described here can calculate extreme gust speeds more accurately than the methods currently in use. Full article

With the rapid development of mega-cities, clarifying the wind field characteristics of high-density urban areas is crucial for the accurate assessment of wind loads on newly built or temporary structures. Taking the high-density urban area of Shanghai as a case study, this research utilizes long-term wind field monitoring data obtained from a super high-rise building under construction. Statistical methods are employed to analyze the mean wind and fluctuating wind characteristics of such sites. The results indicate the following: the mean wind direction distribution is generally consistent with code statistics, with dominant wind directions varying significantly by season; the mean wind profile exponent at the site is 0.39, which is slightly higher than the reference value for Terrain Category D specified in codes; turbulence intensity tends to stabilize as wind speed increases, and the ratio of along-wind to cross-wind turbulence intensity is 1:0.59, which is slightly lower than the code-suggested value and shows a significant positive correlation with the gust factor. The mean peak factor is 2.52, while the mean longitudinal and lateral turbulence integral length scales are 118 m and 45 m, respectively. For strong wind samples, the longitudinal wind spectrum agrees well with the Davenport spectrum, whereas the lateral power spectrum correlates well with the Von Karman spectrum. This study provides a scientific basis and data support for wind load calculation and structural safety assessment in Shanghai and other high-density cities. Full article

The purpose of this article is to develop optimal control strategies for discrete-time multi-agent systems (DT-MASs) with unknown disturbances, with the goal of enhancing their consensus performance and disturbance rejection capabilities. Complex flight conditions, such as the scenario of multi-unmanned aerial vehicle (multi-UAV) maintaining consensus under strong wind gusts, pose significant challenges for MAS control. To address these challenges, this article develops an optimal controller for UAV-based MASs with unknown disturbances to reach consensus. First, a novel improved nonlinear extended state observer (INESO) is designed to estimate disturbances in real time, accompanied by a corresponding disturbance compensation scheme. Subsequently, the consensus error systems and cost functions are established based on the disturbance-free DT-MASs. Building on this, a policy iterative algorithm based on a momentum-accelerated Actor–Critic network is proposed for the disturbance-free DT-MASs to synthesize an optimal consensus controller, whose integration with the disturbance compensation scheme yields an optimal disturbance rejection controller for the disturbance-affected DT-MASs to achieve consensus control. Comparative quantitative analysis demonstrates significant performance improvements over a standard gradient Actor–Critic network: the proposed approach reduces convergence time by 12.8%, improves steady-state position accuracy by 22.7%, enhances orientation accuracy by 42.1%, and reduces overshoot by 22.7%. Finally, numerical simulations confirm the efficacy and superiority of the method. Full article

Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, this paper proposes a prediction model integrating Singular Spectrum Analysis (SSA), Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and a global average pooling layer, combined with inverse adaptive transfer learning. First, SSA is applied to reduce noise in the collected wind power operation data and extract key information. Subsequently, a prediction model is constructed based on TCN, CNN, and global average pooling. The model employs dilated causal convolutions to capture long-term dependencies and uses two-dimensional convolution kernels to extract local mutation features. Furthermore, a domain-adaptive transfer learning module is designed to adjust the model’s parameter weights via backward optimization based on the Maximum Mean Discrepancy (MMD) between the source and target domains. Experimental validation is conducted using real-world wind power operation data from a wind farm in Guangxi, containing 3000 samples sampled at 10 min intervals specifically during severe typhoon periods. Experimental results demonstrate that even with only 60% of the target data, the proposed method outperforms the traditional TCN neural network, reducing the Root Mean Square Error (RMSE) by 58.1% and improving the Coefficient of Determination (R²) by 32.7%, thereby verifying its effectiveness in data-scarce extreme scenarios. Full article