Wind Disturbances Research Articles

Deep Reinforcement Learning-Based Wind Disturbance Rejection Control Strategy for UAV

Deep Reinforcement Learning-Based Wind Disturbance Rejection Control Strategy for UAV

Effects of structural parameters of pressure-swirl nozzle on atomization and dust removal characteristics.

To further improve the dust removal efficiency of water spray, the influence of key structure parameters of nozzle on atomization characteristic (droplet size, atomization angle, flow rate, effective spray distance, wind disturbance resistance) was studied. The results showed that the nozzle had good atomization performance when the outlet diameter was 1.5mm. The internal structure of the nozzle had an obvious influence on the atomization characteristics. The shrinkage angle had a prominent effect on the droplet size and atomization angle. When the shrinkage angle was increased from 60 to 120°, the droplet size and atomization angle were improved by 29.2% and 45.5%, respectively, while the increased shrinkage angle from 120 to 150° only improved by 9.1% and 4.2%, respectively. In addition, the diameter of the center hole had a strong correlation with the effective spray distance. When the diameter of the center hole increased from 1 to 2mm, the effective spray distance increased by 60.9% (to 7.4m) at the pressure of 4MPa, while the effective spray distance without change increased when the diameter of the center hole increased from 2 to 2.5mm. It was determined that the nozzle with the outlet aperture of 1.5mm, the shrinkage angle of 120°, and the diameter of the center hole of 2mm had good atomization and dust control characteristics. Additionally, it was verified that the optimized nozzle had a substantial improvement in controlling respirable dust, and the dust removal efficiencies for PM2.5 and PM5 were increased by 14.29% and 16.52%, respectively, compared to the original nozzle. This study provided guidance for choosing the nozzle of hydraulic support to form the effective dust control spray.

Distributed robust tracking control for multiple unmanned aerial vehicles: theory and experimental verification

The distributed trajectory tracking problem for multiple UAVs under unknown external disturbance is investigated. A new nonlinear robust trajectory tracking strategy for multiple UAVs is proposed. The dynamic model for the UAVs' formation is illustrated in the Tangent frame. For the trajectory tracking problem, a Robust formation tracking control strategy based on robust integral of the signum of error (RISE) is designed to compensate for the effects of unknown external disturbances, which improves the robustness of the UAVs' formation system. Based on the Lyapunov stability analysis, it is proved that the global asymptotic convergence of the coordination errors and the semi-global asymptotic convergence of the UAVs' position tracking errors are achieved. The proposed formation control strategy is validated via real-time experiments that are performed on the self-build UAVs' formation flight control testbed. Flights without wind disturbance and flights with disturbance are all performed. The performance comparison experiment with the conventional sliding mode control algorithm is performed. The experimental results show that the designed control strategy can achieve good coordinated trajectory tracking of multiple UAVs, and has better control performance compared with normal sliding mode control law.

A Cooperative Control Method for Wide-Range Maneuvering of Autonomous Aerial Refueling Controllable Drogue

In the realm of autonomous aerial refueling missions for unmanned aerial vehicles (UAVs), the controllable drogue represents a novel approach that significantly enhances both the safety and efficiency of aerial refueling operations. This paper delves into the issue of wide-range maneuverability control for the controllable drogue. Initially, a dynamic model for the variable-length hose–drogue system is presented. Based on this, a cooperative control framework that synergistically utilizes both the hose and the drogue is designed to achieve wide-range maneuverability of the drogue. To address the delay in hose retrieval and release, an open-loop control strategy based on neural networks is proposed. Furthermore, a closed-loop control method utilizing fuzzy approximation and adaptive error estimation is designed to tackle the challenges posed by modeling inaccuracies and uncertainties in aerodynamic parameters. Comparative simulation results show that the proposed control strategy can make the drogue maneuvering range reach more than 6 m. And it can accurately track the time-varying trajectory under the influence of model uncertainty and wind disturbance with an error of less than 0.1 m throughout. This method provides an effective means for achieving wide-range maneuverability control of the controllable drogue in autonomous aerial refueling missions.

Autonomous longitudinal separation control of aircraft in random wind fields based on MPC

Aiming at the problem of autonomous longitudinal separation under random disturbance of flight path, the random factor of high-altitude wind often leads to poor robustness of longitudinal separation between two aircraft. This paper proposes an autonomous longitudinal separation control method for aircraft based on model predictive control. Firstly, the nonlinear kinematic differential equation of the wind field difference between the two aircraft and the longitudinal separation is established, and the linear time-varying prediction model is derived. The longitudinal separation and path deviation distance of the two aircraft are selected as the optimization objectives, the high-altitude wind is the random disturbance, and the true airspeed and yaw angle of the leading aircraft are measurable. Terminal equality constraints are added to the air safety and aircraft performance constraints to maintain the stability of the system. To verify the effectiveness of the algorithm, within the prescribed simulation time of 120 seconds, this paper sets three groups of different expected intervals 12, 13, 14km. Through the designed MPC controller, by controlling the true airspeed and yaw angle of the trailing aircraft within the rolling time domain period, the interval curve between the two aircraft is relatively smooth and is always not less than the minimum safety interval of 10 km. It stabilizes at the expected target interval at the 74th, 90th and 118th seconds respectively, and begins to return to the route at the 58th, 74th and 95th seconds, and finally returns to the center line of the route; two groups of wind field control groups are set up, one group is the forecast wind intensity increased by 2 times, and the other group is the turbulent wind disturbance intensity increased by 8 times. Both can smoothly and stably establish the expected interval of 12 km at the 61st and 72nd seconds respectively.

Unmanned Surface Vessel–Unmanned Aerial Vehicle Cooperative Path Following Based on a Predictive Line of Sight Guidance Law

This paper explores the cooperative control of unmanned surface vessels (USVs) and unmanned aerial vehicles (UAVs) in maritime rescue and coastal surveillance. The USV-UAV system faces challenges of disturbances and substantial inertia-induced overshooting during path following. A novel position prediction line of sight (LOS) guidance law is proposed to address these issues for USV path following control. Radial basis function-based neural networks (RBF-NNs) are used to estimate disturbances, and a high-order differentiator is used to design a velocity observer for unknown USV velocity. The UAV control system employs proportional–derivative (PD) control with feedforward compensation for quadrotor control design and utilizes a finite-time converging third-order differentiator to differentiate non-continuous functions. The simulation results demonstrate strong robustness in the proposed USV-UAV cooperative control algorithm. It achieves path following control in the presence of wind and wave disturbances and exhibits minimal overshoot.

RNN-based linear parameter varying adaptive model predictive control for autonomous driving

Autonomous driving is a complex and highly dynamic process that ensures controlling the coupled longitudinal and lateral vehicle dynamics. Model predictive control, distinguished by its predictive feature, optimal performance, and ability to handle constraints, makes it one of the most promising tools for this type of control application. The content of this article handles the problem of autonomous driving by proposing an adaptive linear parameter varying model predictive controller (LPV-MPC), where the controller's prediction model is adaptive by means of a recurrent neural network. The proposed LPV-MPC is further optimised by a hybrid Genetic and Particle Swarm Optimization Algorithm (GA-PSO). The developed controller is tested and evaluated on a challenging track under variable wind disturbance.

Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

The application of floating offshore wind turbines means better exploitation of offshore wind resources. However, the six-degree of freedom motion characteristics of floating platforms bring greater challenges to control system design. Based on the dynamic characteristics of semi-submersible floating offshore wind turbines, this paper establishes a simplified nonlinear dynamic model for control system design. On this basis, a complete framework for nonlinear model predictive control of maximum wind energy extraction for semi-submersible floating offshore wind turbines considering wind and wave disturbances is developed. Based on the previewed wind and wave, a dynamic optimization problem with both state and control constraints is constructed, considering maximum wind energy extraction and torque fluctuation. Then, an improved equilibrium optimizer is proposed to address the nonlinear non-convex dynamic optimization problems, which achieves a better trade-off between exploitation and exploration. Simulation results verify the superiority of the proposed nonlinear model predictive control framework via the improved equilibrium optimizer, and the influences of different control algorithms on the platform motion state, power coefficient, and equivalent wind speed are analyzed.

Underestimation of global soil CO2 flux measurements caused by near-surface winds

Soil respiration (Rs) is the largest source of atmospheric CO2, and an accurate understanding of the relationship between near-surface winds, CO2 release from the soil surface, and measurement methods is critical for predicting future atmospheric CO2 concentrations. In this study, the relationship between wind speed and soil CO2 fluxes is elucidated on a global scale through meta-analysis, and the flux measurement methodology is further explored in conjunction with the results of a controlled trial to clarify the uncertainty of the measurement results. The results indicate that near-surface wind speed is positively correlated with soil CO2 release and that near-surface winds result in increased soil CO2 gas release. Wind disturbance affects both the concentration gradient and gas chamber measurements, and the lower calculated soil CO2 release conflicts with the notion that the wind pump effect and Bernoulli effect of negative pressure cause a greater surface gas exchange. The results of the log-response ratios indicate that near-surface winds lead to an underestimation of 12.19–19.75% in widely-used gas chamber method measurements. The results of this study imply that some of the current Rs measurements are biased and that the influence of near-surface winds on Rs measurements needs to be urgently addressed to assess the terrestrial carbon cycle more accurately and develop climate change response strategies.

Active Disturbance Rejection Flight Control and Simulation of Unmanned Quad Tilt Rotor eVTOL Based on Adaptive Neural Network

The unmanned quad tilt-rotor eVTOL (QTRV) is a variable-configuration aircraft that combines the features of vertical takeoff and landing (VTOL), hovering, and high-speed cruising, making its control system design particularly challenging. The flight dynamics of the QTRV differ significantly between the VTOL and cruise modes, and are further influenced by rotor tilt and external wind disturbances. Developing a unified, highly coupled nonlinear full-flight dynamics model facilitates flight control system design and simulation verification. To ensure stable tilt of the QTRV, a tilt corridor was established, along with the design of its tilt route and manipulation strategy. An adaptive neural network active disturbance rejection controller (ANN-ADRC) is proposed to ensure stable flight across all modes, reducing the control parameters and simplifying tuning while effectively estimating and compensating for unknown disturbances in real time. A hardware-in-the-loop (HIL) simulation system was designed for full-mode flight control simulation, and the results demonstrated the effectiveness of the proposed control method.

PID Controller Based on Improved DDPG for Trajectory Tracking Control of USV

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks.

A Proposed Method for Assessing the Spatio-Temporal Distribution of Carcharhinus melanopterus (Quoy and Gaimard, 1824) in Shallow Waters Using a UAV: A Study Conducted in Koh Tao, Thailand

In this study, we propose a method for assessing the temporal and spatial distribution of Carcharhinus melanopterus in shallow waters using unmanned aerial vehicles (UAVs). Aerial surveys were conducted in Tien Og Bay (Koh Tao, Thailand) thrice daily (morning, afternoon, evening) along a 360 m transect at a 30 m altitude. Environmental factors, including cloudiness, sea conditions, wind, tide, and anthropogenic disturbance, were recorded for each time slot. We developed a Python/AppleScript application to facilitate individual counting, correlating sightings with GPS data and measuring pixel-based length. Abundance varied significantly across time slots (p < 0.001), with a strong morning preference, and was influenced by tide (p = 0.040), favoring low tide. Additionally, abundance related to anthropogenic disturbance (p = 0.048), being higher when anthropogenic activity was absent. Spatial distribution analysis indicated time-related, sector-based abundance differences (p < 0.001). Pixel-based length was converted to Total Length, identifying juveniles. They exhibited a strong sector preference (p < 0.001) irrespective of the time of day. Juvenile abundance remained relatively stable throughout the day, constituting 94.1% of afternoon observations. Between 2020 and 2022, an underwater video survey was conducted to determine the sex ratio of the individuals. Only females and juveniles were sighted in the bay.

Battery Condition Monitoring of Quadrotor UAV Using Machine Learning Classification Algorithm

Unmanned aerial vehicle flight performance and efficiency rely on various factors. Flight instabilities can happen due to malfunctions inside the system and disturbances from the external environment. Battery status plays a significant role in healthy flight conditions. A weak battery will affect the performance of propellers and motors, and the presence of wind disturbance can contribute towards inefficient flying capabilities. Therefore, investigation of fault at the early stage is crucial to maintain the great performance of the UAV. This paper aims to investigate the best prediction system from the existing machine learning algorithm such as Decision Tree (DT), Linear Discriminant (LD), Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Neural Network (NN) to classify the battery condition of the quadrotor by extracting the features from the displacement time series dataset. By using recorded flight data, it will be statistically analyzed to extract the flying condition features. The extracted features are the Euclidian distance (ED), speed, acceleration, Periodogram Power spectral density (PSD) and Fast Fourier Transform (FFT) of the signal. The result shows that the two best classifier algorithms are the Decision Tree and Neural Network models with training accuracy of 98% and 93% in Set A and B, respectively.

Stability analysis of flying wing layout aircraft based on radial basis function neural network model

PurposeTo ensure the stability of the flying wing layout unmanned aerial vehicle (UAV) during flight, this paper uses the radial basis function neural network model to analyse the stability of the aforementioned aircraft.Design/methodology/approachThis paper uses a linear sliding mode control algorithm to analyse the stability of the UAV's attitude in a level flight state. In addition, a wind-resistant control algorithm based on the estimation of wind disturbance with a radial basis function neural network is proposed. Through the modelling of the flying wing layout UAV, the stability characteristics of a sample UAV are analysed based on the simulation data. The stability characteristics of the sample UAV are analysed based on the simulation data.FindingsThe simulation results indicate that the UAV with a flying wing layout has a short fuselage, no tail with a horizontal stabilising surface and the aerodynamic focus of the fuselage and the centre of gravity is nearby, which is indicative of longitudinal static instability. In addition, the absence of a drogue tail and the reliance on ailerons and a swept-back angle for stability result in a lack of stability in the transverse direction, whereas the presence of stability in the transverse direction is observed.Originality/valueThe analysis of the stability characteristics of the sample aircraft provides the foundation for the subsequent establishment of the control model for the flying wing layout UAV.

A Hinge Moment Alleviation Control Strategy for Morphing Tail Aircraft Based on a Data-Driven Method

Morphing airplane technology is currently a focal point of research. For morphing airplanes, besides effective morphing strategies and control schemes, the hinge moment at the root of the vertical tail during morphing is a critical factor influencing flight safety. To prevent failure in tail morphing due to excessive hinge moments, this paper analyzes the hinge moment characteristics of the variable vertical tail structure in high-speed flight, based on a flying wing model from the China Aerodynamics Research and Development Center. The proposed adaptive morphing tail hinge moment reduction (AMTHR) method is model-free, utilizing real-time data to dynamically adjust the rudder and reduce hinge moments without requiring prior knowledge of system dynamics. This method utilizes the concept of extremum-seeking control by introducing periodic perturbations to the system and adjusting the control input based on their impact on the output. This approach drives the output toward an extremum point, enabling real-time reduction of the vertical tail hinge moment. Finally, the simulation analysis is carried out under the conditions of no wind and gust disturbance, and the effect of this method on the load reduction of the tail hinge moment is verified.

Recent results and outstanding questions on the response of the electrodynamics of the low latitude ionosphere to solar wind and magnetospheric disturbances

Storm-time ionospheric electrodynamics effects have been the subject of extensive studies. The solar wind/magnetosphere/ionosphere and thermosphere disturbance wind dynamos have long been identified as the main drivers of low latitude storm-time electrodynamics. Extensive detailed studies showed that climatology of low latitude disturbance electric fields and currents is in good agreement with results from global theoretical and numerical models. Over the last decade, however, numerous studies have highlighted that the response of low latitude electrodynamics to enhanced geomagnetic activity is significantly more complex than previously considered. It is now clear that the electrodynamic disturbance processes are affected by a larger number of solar wind and magnetospheric parameters and that they also have more significant spatial dependence. This is especially pronounced during and after large geomagnetic storms when multiple simultaneous disturbance processes are also active. In this work, we briefly review the main past experimental and modeling studies of low latitude disturbance electric fields, highlight new results, discuss outstanding questions, and present suggestions for future studies.

Substantial increase of organic carbon storage in Chinese lakes

Previous studies typically assumed a constant total organic carbon (OC) storage in the lake water column, neglecting its significant variability within a changing world. Based on extensive field data and satellite monitoring techniques, we demonstrate considerable spatiotemporal variability in OC concentration and storage for 24,366 Chinese lakes during 1984–2023. Here we show that dissolved OC concentration is high in northwest saline lakes and particulate OC concentration is high in southeast eutrophic lakes. Along with increasing OC concentration and water volume, dissolved and particulate OC storage increase by 44.6% and 33.5%, respectively. Intensified human activities, water input, and wind disturbance are the key drivers for increasing OC storage. Moreover, higher OC storage further leads to an 11.0% increase in nationwide OC burial and a decrease in carbon emissions from 71.1% of northwest lakes. Similar changes are occurring globally, which suggests that lakes are playing an increasingly important role in carbon sequestration.

Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control

Wind turbines experience significant unbalanced loads during operation, exacerbated by external disturbances that challenge the stability of the pitch control system and affect output power. This paper proposes an independent pitch adaptive control strategy integrating state feedback and disturbance accommodating control (DAC). Initially, nonlinear wind turbine dynamics are globally linearized, and DAC is applied to mitigate the impact of wind disturbances dynamically. Subsequently, the entire range of wind speeds is segmented, and controllers are individually designed to optimize gain settings according to specific control objectives at each wind speed interval. Scheduling parameters such as collective pitch angle and tower fore-aft displacement are identified and trained using Radial Basis Function Neural Networks (RBFNN). Finally, based on the output gain values determined by RBFNN, the full-state feedback controller group is adaptively adjusted, and the optimal controller is selected for the final output. Simulations conducted on the NREL 5MW reference wind turbine model using FAST and Simulink demonstrate that compared to the ROSCO controller, the proposed strategy ensures smoother output power and effectively reduces blade and tower loads, thereby extending the turbine’s operational lifespan.

Observational Evidence for the Neutral Wind Responses in the Mid‐Latitude Lower Thermosphere to the Strong Geomagnetic Activity

AbstractBased on two meteor radars in mid‐latitudes of China, the mid‐latitude lower thermospheric neutral wind responses to the 2015 St. Patrick's Day great storm are investigated. The AE and PCN indices presented the similar quasi‐5‐hour oscillations during the storm. Interestingly, the analogous and close‐correlated storm‐time quasi‐5‐hour oscillations were also observed in both the meridional wind differences at 90–102 km derived from meteor radars. The meridional wind disturbances in the lower thermosphere also showed the extension toward the lower latitudes. It has been found that the enhanced equatorward wind disturbances at 250 km estimated by the Horizontal Wind Model‐14 and Fabry‐Perot Interferometer (FPI) emerged accordingly with the increases of AE and PCN with a time delay. And the enhancements of equatorward (poleward) wind disturbances at 250 km were accompanied by the increments of equatorward (poleward) wind disturbances at 94 km with a time lag of a few hours. It is thus suggested that the multiple intensified Joule heating events with quasi‐5‐hour time intervals were triggered by the successive substorm expansions during the storm. Then the Joule heating events led to the vertical wind and temperature disturbances in the mid‐latitude lower thermosphere via disturbing the thermospheric meridional circulation, which consequently induced the quasi‐5‐hour meridional wind disturbances therein.

Implications of Solar Wind Disturbances on Forbush Decrease

This study investigated the role of solar wind disturbances on variations in cosmic ray intensity. To conduct the study, use was made of the data on cosmic rays from the SOPO, CLMX, and MOSC neutron stations, as well as solar wind speed data from 2000 to 2005. The source Forbush decrease (FD) dates were generated from onset journal publications, specifically the FD list of Dumbovi ́c et al., (2011). The manual method of FD selection was used to identify FDs. The FD dates, computed magnitudes, and solar wind speed data were recorded and presented. From the study, FDs were generated, some of which were of the same date as those in the source table, while others were not observed in the source table. FDs not observed in the source table were generated and catalogued by this research. It was also observed that the magnitude of FDs generally depends on the coordinates of the observing neutron stations. A strong correlation with a value of cc = 0.93 was observed between FDs of SOPO and MOSC stations, followed by FDs of CLMX and MOSC stations of value cc = 0.89, and lastly, FDs of SOPO and CLMX stations of value cc = 0.74. This implies that observed FDs from neutron stations are coordinate-dependent. The correlation between FD magnitudes and solar activities shows that solar wind had a high and significant correlation with FD magnitude to the tune of cc = 0.54. Based on the findings, the study concludes that solar wind disturbances play a crucial role in causing a sharp decrease in the intensity of cosmic rays known as Forbush decrease.