风对多旋翼无人机的影响和作用

本文针对变结构四旋翼机器人在灾害救援与侦察任务中的飞行控制问题，提出一种基于改进自抗扰控制(ADRC)的方法。通过设计新型非线性函数lnfal替代传统fal函数，增强扩张状态观测器(ESO)对系统内外扰动的估计精度与补偿能力，从而提高控制器的抗干扰性和响应性能。在Matlab/Simulink环境中进行仿真验证，结果表明所提方法能够有效提升轨迹跟踪精度，抑制超调与振荡，加快收敛速度，表现出优于传统ADRC的鲁棒性和动态性能，适用于复杂干扰环境下的高精度控制任务。

本文探究了存在建模不确定性和外部扰动以及存在执行器故障情况下的轨迹跟踪控制问题，提出了一种基于径向基神经网络的模糊自适应容错控制策略。首先，引入虚拟控制量对四旋翼无人机系统进行解耦，使其转换为全驱动模型，以简化系统的数学模型，通过定义执行器故障函数，推导出四旋翼无人机执行器故障下的动力学模型，并利用径向基函数神经网络对未知非线性函数的逼近特性处理四旋翼无人机模型中的不确定性项和外部扰动。为了提高系统的收敛速度和稳定性，利用模糊逻辑系统处理所提出的滤波变量，从而优化系统控制器的设计。最后，根据李雅普诺夫候选函数设计自适应控制律和参数更新律，以确保本文所提控制算法的有效性和鲁棒性。通过MATLAB/Simulink仿真实验验证了所提出的模糊自适应容错控制策略的有效性。

本文提出了一种基于观测器的自适应滑模容错控制方法，用于四旋翼无人机在存在外部未知干扰、执行器存在加性故障和乘性故障条件下的稳定控制。该方法采用观测器实时监测系统中的外部干扰和加性故障，并设计自适应律来应对乘性故障，实现对故障的动态补偿。四旋翼无人机的控制策略采用内外环结构，其中内环姿态控制采用全局快速终端滑模控制，以确保在干扰作用下的快速收敛和全局稳定性；外环位置控制则通过积分滑模控制提高系统的抗干扰能力和跟踪精度。仿真结果表明，该方法在执行器故障和扰动条件下具有优异的容错性和鲁棒性，为无人机在复杂环境下的稳定飞行提供了有效保障。

... 控制站 ... 四旋翼无人机技术参数如表1所示。 在技术参数上，该四旋翼无人机具有自动增温、地面控制变焦、续航时间长、抗风能力强等特点，基本满足现场应用需要。

本文旨在研究无人机在高空定点投放物品时，不能飞到目标正上方投放，而是要适当提前投放才能命中目标，投放后由于惯性、阻力等影响，会做类平抛运动，所以要适当提前投放才能命中目标。我们分析了无人机投放距离与飞行速度、飞行高度、空气阻力等因素之间的关系，以及无人机在发射爆炸物时俯冲较角度、飞行速度、发射速度对等因素对于命中精度的影响，并通过数学建模带入数据计算给出最优策略。我们建立数学模型，描述出无人机投放距离与无人机飞行高度、飞行速度、空气阻力等之间的关系。明确假设条件，无人机以平行于水平面的方式飞行，这意味着我们可以将无人机的运动分解为水平面和竖直方向上的运动。通过类平抛运动与受力分析。之后确定物理模型：考虑无人机在发射爆炸物前的飞行轨迹，可以将其看作是一个抛体运动。根据牛顿第二定律和重力加速度的定义，可以列出抛体运动的方程组。确定目标函数和约束条件：目标函数可以设置为无人机发射距离与目标的直线距离之间的差值的平方。最后，需要建立一个稳定性与命中精度之间的关系模型，还需要在一定条件下，给出无人机的最优飞行姿态调整策略。建立稳定性与命中精度之间的关系模型，无人机的飞行稳定性可以通过无人机的姿态角来描述。在无人机的飞行过程中，姿态角不断变化，因此需要考虑无人机姿态角的变化对命中精度的影响。

为了提升桥梁病害检测无人机航线规划仿真能力，能方便高效地进行航线规划仿真研究，设计了一套基于RRT (Rapid-exploration Random Tree)快速扩展随机树算法的航线规划仿真系统。该系统包含了环境模块，算法模块以及GUI模块，可实现对桥梁环境进行转换和利用GUI进行算法调参，简化仿真环节，直观规划过程。最后通过实验仿真验证，证实其方便快捷，可满足航线规划研究需要。

本文针对四旋翼无人机角速度传感器出现偏置故障导致姿态控制精度下降的问题，提出了一种基于自适应滑模控制的容错控制方法。该方法通过自适应律在线估计传感器偏置故障量，并将其补偿到滑模控制器中，以修正姿态误差。论文构建了带有故障的无人机动力学模型，设计了自适应滑模控制器，并利用Lyapunov稳定性理论和Barbalat引理证明了系统的稳定性。最后，通过MATLAB/Simulink仿真验证了所提方法的有效性，表明该方法能够有效稳定机体姿态，实现对目标姿态的精确跟踪。

With the rapid development of the low-altitude economy, multirotor unmanned aerial vehicles (UAVs) have been widely applied to logistics delivery, inspection and monitoring, and emergency rescue missions, where the operating environments are often accompanied by complex airflow disturbances. Accurate control of rotor lift is required to enhance flight safety and mission reliability; however, traditional multirotor UAV powertrain system lacks the ability to control rotor thrust with high accuracy. To overcome this limitation, we propose a thrust feedback control strategy that enhances control performance through closed-loop rotor thrust regulation. First, we develop a lightweight embedded sensor mounted between the UAV’s airframe and motor, enabling direct measurement of rotor-generated thrust. The strategy eliminates the thrust-to-speed mapper and incorporates a thrust feedback control module. Furthermore, we establish the powertrain system model that takes motor speed control quantity as the input and rotor thrust as the output. Based on this model, a closed-loop controller is designed to achieve precise thrust regulation. Finally, experimental results indicate that thrust closed-loop control improves control accuracy and stability while effectively suppressing wind disturbance, thereby validating the effectiveness of the proposed strategy.

Abstract The ability of multirotor unmanned aerial vehicles (UAVs) to perform accurately in windy environments is crucial for extended use in outdoor applications. To design UAVs to operate in these environments, most studies have focused on static performance metrics such as thrust-to-weight ratio and endurance, without directly considering closed-loop control performance. This work develops a simplified metric that serves as a predictor for achievable disturbance rejection performance, enabling efficient UAV design selection without requiring full-scale nonlinear simulations. A reduced-order model is introduced to capture key aerodynamic and actuation characteristics, allowing for rapid evaluation of UAV configurations. The metric is validated against high-fidelity nonlinear simulations, demonstrating strong correlation with actual control performance. By bridging the gap between UAV structural optimization and closed-loop control behavior, this approach provides a practical tool for integrating disturbance rejection capabilities into UAV design processes. The practical utility of this metric is supported by experimental findings from related wind tunnel studies of fully-actuated UAVs, which demonstrate that actual disturbance rejection performance aligns with the trends predicted by the simplified correlation function.

No abstract available

To address the challenges of strong nonlinear coupling and multi-source disturbance sensitivity in multi-rotor eVTOLs operating in low-altitude dense-disturbance environments, an SMC-NDI cascade control architecture is proposed. The nonlinear characteristics and dynamic behaviors of multi-rotor eVTOLs are analyzed, and a dynamic model is established. Based on singular perturbation theory, the vehicle states are divided into four independent loops according to response speeds. The nonlinear decoupling for each sub-loop is achieved via NDI-based control laws. A sliding mode variable structure theory is utilized to design pseudo-control inputs for the dynamic inversion controller, while an exponential reaching law is employed to suppress model uncertainties and external disturbances. Simulation results demonstrate that, under parameter perturbations and wind disturbances, the proposed SMC-NDI controller reduces the peak hover position error from ± 1.2 m to ± 0.3 m and decreases the trajectory tracking RMSE by 52%, compared to traditional dynamic inversion methods. Additionally, attitude angle overshoot is confined within ± 1.5°, validating the controller’s comprehensive advantages in dynamic response, robustness, and decoupling performance.

In this paper we propose a disturbance observer based control for the class of multirotor aerial vehicles having co-planar and collinear propellers, following a geometric approach. The control scheme is based on the well known inner-outer loop structure, where the tracking control problem on the group of rotations is extended with an observer. To prove the asymptotic stability of the tracking error, a Lyapunov stability analysis is provided, taking into account kinematics, dynamics, and disturbance observer errors. For this purpose, disturbance rejection is achieved leveraging the disturbance model, which is assumed to be generated by exogenous systems having arbitrary orders. Simulations are performed on a hexarotor under elaborate external disturbances, which take into account unmodeled dynamics and time-varying wind effects. Simulation results show that the proposed control scheme can compensate for the disturbances, even when the embedded exogenous system model is coarse, outperforming the baseline geometric controller without disturbance compensation.

This paper presents a novel control framework for enhancing the attitude stabilization of multirotor UAVs using Control Moment Gyros (CMGs) and a Disturbance Robust Drive Law (DRDL). Due to their lightweight and compact structure, multirotor UAVs are highly susceptible to disturbances such as wind, making it challenging to achieve stable attitude control using rotor thrust alone. To address this issue, we employ CMGs to provide robust attitude control and apply Fast Terminal Sliding Mode Control (FTSMC) to ensure fast and accurate convergence within a finite time. The combination of CMGs’ torque amplification capability with the DRDL enables the system to effectively avoid singularities and maintain stable control performance in the presence of disturbances. Simulation results demonstrate that the CMG-equipped hexarotor utilizing the DRDL rapidly converges to the target attitude despite external disturbances, while minimizing oscillations in both motor speed and gimbal movement. Additionally, compared to the pseudo-inverse control method, the proposed approach significantly improves singularity avoidance and disturbance mitigation. The proposed control framework enhances the stability and reliability of UAV operations and demonstrates its potential for high-performance control in challenging disturbance environments.

No abstract available

The beneficial aspects of fully-actuated multirotor UAVs, provided by nonparallel rotor configuration, are increasingly being recognized and utilized to great benefit in high-precision applications. Full six-degree-of-freedom force control, higher control bandwidth and improved disturbance rejection prove valuable. However, the cant angle will cause great multirotor dihedral effect and significantly affects blade flapping, which decreases the flight performance of nonparallel actuated UAVs. Therefore, this paper presents a novel aerodynamic model for fully-actuated hexrotor UAVs while considering the aerodynamic effects caused due to tilt angled propeller configurations. In the proposed aerodynamic model, the significance of multirotor dihedral effect, defined as an aerodynamic coefficient proportional to the relative linear velocity of the UAV, is modeled for nonparallel actuators. Additionally, the modeling for blade flapping effect for cant angled propellers is provided to accurately model the aerodynamics. Wind tunnel experiments were conducted to characterize the aerodynamic constants for multirotor dihedral effect, blade flapping effect and air friction. Experimental results are presented to validate the proposed aerodynamic model on a fully-actuated hexrotor UAV (Purdue’s Dexterous Hexrotor). Lastly, the multirotor dihedral effect and blade flapping effect at different cant angles and at different wind speeds are analyzed.

No abstract available

In recent years, the popularity of small multirotor unmanned aerial vehicles and, in particular, quadrocopters (QC) has increased significantly, due to both their characteristics and the low cost of manufacturing and operation. At the same time, one of the promising ways to increase the efficiency of using QC to solve a wide variety of tasks in both civilian and military spheres is to improve the flight control algorithms of the spacecraft. However, despite the presence of numerous classical and modern methods for synthesizing flight control algorithms, many of them turn out to be ineffective in conditions of a priori uncertainty of the mathematical model of vehicle dynamics, as well as in conditions of wind disturbance. QC as an object of control is a complex, nonlinear, multidimensional, multi-connected dynamic system of the 12th order with the presence of indeterminate parameters and external disturbances. The paper considers the problem of synthesizing an algorithm for the subsequent flight control of a spacecraft, which provides tracking by a vector of controlled variables of its arbitrarily set, programmatic change. As indicators of the effectiveness of the synthesized monitoring control algorithm, the QC summer uses, firstly, direct indicators of the quality of the control process (control time and overshoot), characterizing the speed and tendency of the system to oscillate, and, secondly, the maximum amplitude of the controlling effects, characterizing the energy consumption for their generation. The article is devoted to a comparative analysis of the parametric robustness properties of QC flight tracking algorithms synthesized on the basis of the most popular modern methods of nonlinear control of dynamic objects: the sliding mode method, the integrator bypass method, the feedback linearization method, the MPC proactive control method, the "deep" feedback method, the method of inverse dynamics problems with compensation for non-linearity. At the same time, the effectiveness of the synthesized flight tracking control algorithms was analyzed by computer verification in the Python environment under conditions of variable load and uncontrolled disturbing wind effects.

We consider the problem of tracking a reference trajectory for dynamical systems subject to a priori unknown state-dependent disturbance behavior. We propose a formulation that embeds the uncertain system into a higher dimensional deterministic system that accounts for worst case disturbances. Our main insight is that a single controlled trajectory of this embedding system corresponds to a controlled forward invariant interval tube around the reference trajectory. By taking observations of the system, we then propose to estimate the state-dependent uncertainty with Gaussian Process regression, which improves the accuracy of the forward invariant tube as data is collected. Given a safety objective, we also provide conditions on when an additional observation of the unknown disturbance behavior needs to be collected to maintain safety. We demonstrate our formulation on a case study of a planar multirotor attempting a safe landing in an unknown wind field.

Enabling the capability of accurate and reliable pre-cision landing for multirotor using deep reinforcement learning is presented in this paper. A realistic quadrotor landing environ-ment was developed using Gazebo, taking wind disturbance into consideration, in order to train a robust control using the Deep Q-Network algorithm. Human-aided demonstrations are presented in order to increase learning speed. It navigates the quadrotor to the landing site through vision-based methods, which include AprilTags detection. We show the feasibility of a smooth sim-to-real transfer with minimum adjustments through extensive simulations and real-world experiments, showing that the landing error is less than 5 cm in real-world tests.

Multirotor Uncrewed Aircraft Systems (UAS), widely known as aerial drones, are increasingly used in various indoor and outdoor applications. For outdoor field deployments, the plethora of UAS rely on Global Navigation Satellite Systems (GNSS) for their localization. However, dense environments and large structures can obscure the signal, resulting in a GNSS-degraded environment. Moreover, outdoor operations depend on weather conditions, and UAS flights are significantly affected by strong winds and possibly stronger wind gusts. This work presents a nonlinear model predictive position controller that uses a disturbance observer to adapt to changing weather conditions and fiducial markers to augment the system’s localization. The developed framework can be easily configured for use in multiple different rigid multirotor platforms. The effectiveness of the proposed system is shown through rigorous experimental work in both the lab and the field. The experimental results demonstrate consistent performance, regardless of the environmental conditions and platform used.

This paper deals with cooperative wind disturbance estimation by multiple quadrotor drones. The authors have developed a cooperative wind disturbance estimation method combining the optimal control and adaptive control. However, only disturbances affecting translational motion have been considered and torque disturbances influencing rotational motion have not been taken into account. This paper addresses a cooperative disturbance estimation problem in a more realistic situation where torque disturbances exist as well. The two types of disturbances are estimated and compensated simultaneously. A disturbance estimator is designed based on the Lyapunov stability theory. Numerical simulations show the effectiveness of the proposed method and also indicate the necessity of simultaneous estimation of translational disturbances and torque disturbances.

This paper focuses on wind resistance technology for agricultural quadrotor drones, addressing the challenge of stable flight control under wind disturbances and variable payload conditions. To enhance anti-interference performance, an improved control algorithm integrating Active Disturbance Rejection Control (ADRC) and PID is proposed. A nonlinear dynamic model and wind disturbance model are established, and a nested dual-loop controller is designed, with parameters optimized via genetic algorithm (GA). Simulation results in MATLAB/Simulink demonstrate that the proposed method reduces roll angle error by 68% and trajectory deviation by 75% under 8 m/s wind conditions, while significantly improving spraying uniformity. The study provides an effective control strategy for agricultural UAVs operating in complex environments, offering both theoretical and practical value for precision agriculture applications.

This article studies an anti-collision control method for the trajectory tracking problem of an unmanned aerial vehicle for aircraft skin inspection under complex wind disturbance. To guarantee the safety of the aircraft during the inspection, the anti-collision control under wind disturbance is described by the maximum position offset constraint of the unmanned aerial vehicles (UAVs). Then, an exponential nonlinear integral super-twisting sliding mode (ENISTSM) position controller is designed, and incorporated with the prescribed performance control(PPC) to ensure that the position error is consistently constrained within specified bounds. Subsequently, the attitude and angular velocity cascaded control law is investigated based on the modified Rodrigues parameters (MRPs) attitude representation using the exponential super-twisting sliding mode (ESTSM) method. The proposed method achieves fast tracking of relatively large variable angles and is robust to wind disturbance. In addition, the stability of the controllers is proven via Lyapunov analysis. Finally, the simulation results are included by considering the turbulent wind field to demonstrate the effectiveness and the advantages.

Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) possess hover and high-speed cruise capabilities, but they are highly sensitive to gusts when operating in rotor mode due to their large wing surface area. This paper designs a rotor-mode controller based on geometric control theory to avoid errors and singularities caused by model linearization or parameterization. A disturbance rejection controller is developed to estimate and compensate for the effects of time-varying wind fields on both the position and attitude loops of the UAV. Prescribed performance control is utilized to constrain the transient and steady-state behaviors of the disturbed UAV.

This paper investigates bearing-based formation control of multiple unmanned aerial vehicles (UAVs) flying in low-altitude wind fields. In such environments, time-varying wind disturbances can distort the formation geometry, enlarge bearing errors, and even induce potential collisions among neighboring UAVs, yet they also contain components that can be beneficial for the formation motion. Conventional disturbance compensation methods treat wind as a purely harmful factor and aim to reject it completely, which may sacrifice responsiveness and energy efficiency. To address this issue, we propose a pure bearing-based formation control framework with Conditional Disturbance Utilization (CDU). First, a real-time disturbance observer is designed to estimate the wind-induced disturbances in both translational and rotational channels. Then, based on the estimated disturbances and the bearing-dependent potential function, CDU indicators are constructed to judge whether the current disturbance component is beneficial or detrimental with respect to the formation control objective. These indicators are embedded into the bearing-based formation controller so that favorable wind components are exploited to accelerate formation convergence, whereas adverse components are compensated. Using an angle-rigid formation topology and a Lyapunov-based analysis, we prove that the proposed CDU-based controller guarantees global asymptotic stability of the desired formation. Simulation results on triangular and hexagonal formations under complex wind disturbances show that the proposed method achieves faster convergence and improved responsiveness compared with traditional disturbance observer-based control, while preserving formation stability and safety.

No abstract available

The present paper presents the design of a foldable quadrotor that can adapt its morphology in the presence of sudden external disturbances such as wind. It focuses on solving the problem of tracking a specific trajectory of the foldable quadrotor in the presence of external disturbances. Therefore, robust controllers were designed to ensure better tracking under sudden turbulence, such as wind, considering the inertia change due to the rotating arms. An architectural console for foldable quadrotors was introduced under a gust of conservative winds. A dual loop control strategy was used in which the movements in the X and Y axes were managed by a nonlinear PID controller in the outer loop. In addition, a sliding mode controller was added to the inner loop to regulate the attitude and height in the Z direction, where the mechanical model of the aircraft was combined with the conservative wind gust model. When a conservative wind gust hit the foldable quadrotor for a certain period, a slight deviation from the proposed path at the beginning and end of the disturbance existed with an overshoot (7.468×10^(-3)) for the x-axis and (8.069×10^(-2)) for the z-axis at the beginning of the disturbance, while at the end of disturbance on the z-axis appears undershoot in its value (6.725×10^(-2)). Therefore, the quadrotor maintained its stability with an accuracy of 98%.

This paper presents a quaternion-based robust sliding-mode controller for quadrotors operating under significant wind disturbances. The proposed control method improves the reliability and efficiency of quadrotor control by eliminating the singularity problem inherent in the Euler angle method. The quadrotor dynamics and wind environment are modeled, and dynamic analysis is performed via numerical simulation. A realistic wind model is used, similar to a combination of deterministic and statistical models. The Lyapunov stability theory is utilized to prove the convergence and stability of the proposed control system. The simulation results demonstrate that the quaternion-based controller enables the quadrotor to follow the desired path and remain stable, even under external wind disturbances. Specifically, both position and attitude converge to the desired values within 10 s, demonstrating stable performance despite the challenging wind disturbances in both scenarios. Scenario 1 features turbulence with an average wind speed of 12 m/s and changing wind directions, while Scenario 2 models an environment with wind speeds that change abruptly and discretely over time, coupled with temporal variations in wind direction. Additionally, a comparative analysis with the conventional PD controller highlights the superior performance of the proposed RSMC controller in terms of trajectory tracking, stability, and energy efficiency. The rotor speeds remain within a reasonable and hardware-feasible range, ensuring practical applicability.

Enhancing quadrotor control to improve both precision and responsiveness is essential for expanding their deployment in complex and dynamic environments. These aerial vehicles are widely used in applications, such as aerial mapping, delivery, disaster response

Path planning is one of the critical components for enabling autonomous unmanned aerial vehicles (UAVs) flight. Existing UAV trajectory planning methods often exhibit insufficient robustness in wind field environments. Therefore, this paper proposes an Artificial Potential Field-based Robust Planner (APFRP) algorithm that incorporates wind field parameters. The core design involves a wind field-attraction coupling mechanism that adjusts the attractive force intensity. This mechanism aims to resolve the problem of UAV being unable to reach their target due to wind influence. Tested in both simulated and real-world wind environments, the UAV demonstrates the ability to generate weak wind field trajectories to avoid strong winds, thereby enhancing trajectory robustness and proving the algorithm's practical utility.

This paper presents a predefined-time circumnavigation strategy for an unmanned aerial vehicle (UAV) modeled by nonholonomic kinematics, tasked with tracking a stationary and a moving target under wind disturbances. Unlike existing methods that offer asymptotic or finite-time convergence without explicit control over convergence time, our framework guarantees that the UAV reaches a desired circular path around the target within a user-specified time bound. We design a Lyapunov-based guidance vector field in the target-relative frame to drive the radial tracking error to zero in predefined-time. To maintain a constant commanded airspeed while compensating for wind and target motion, we derive a scaling factor that adjusts the guidance vector field while preserving its Lyapunov-based convergence properties. A nonlinear program is solved at each timestep to ensure the UAV’s heading converges to the desired heading angle within the predefined-time while respecting actuator limits. Numerous simulation results depicting various cases are presented justifying the efficacy of the proposed methodology.

This paper proposes a formation control strategy for quadrotor unmanned aerial vehicles (UAVs) operating under wind disturbances, utilizing solely bearing measurement information. Initially, the dynamic model that incorporates the effects of wind disturbances on both position and orientation is established. Subsequently, adaptive estimation techniques are employed to compensate for the adverse impacts of wind disturbances on the flight performance of the quadrotor UAV system, thereby enhancing its operational stability and reliability. Next, the negative gradient method is applied to optimize the UAV system towards achieving the desired bearing rigid formation. Meanwhile, the backstepping method is utilized to design the formation control law based exclusively on bearing measurements. Additionally, consensus algorithms are implemented to ensure uniformity in the attitudes of the UAVs, thus maintaining alignment within the formation. Ultimately, simulation results validate the effectiveness of the proposed formation control algorithm.

A self-organizing bidirectional fuzzy brain emotional learning (SO-BFBEL) controller is developed to control a quadcopter UAV in an uncertain environment. The proposed SO-BFBEL controller improves the performance of the existing BFBEL controller by generating more accurate fuzzy layers in real-time and removes the need to depend on expert knowledge to set the fuzzy layers. The proposed SO-BFBEL controller is applied to control the position of a quadcopter UAV for accurate 3-D eight shape trajectory tracking for three different speed settings under extreme wind disturbances up to 5 m/s in real-time experimentation. Two industrial fans are used to create the wind disturbance for the experiments. The performance is compared to the DNN-MRFT based PID controller and to the BFBEL controller. The experimental results show that the proposed SO-BFBEL controller achieves robust trajectory tracking for both circle and 3-D eight shaped trajectory under extreme wind disturbance and with lower computational cost. The proposed self-organizing algorithm can be applied to optimize other controllers with fuzzy neural network structure. Note to Practitioners—The learning rate alpha and beta are set manually at the beginning (if no simulations are done to test) to check for the appropriate magnitude of the control signal. If the learning rates are too low, then the controller output will be too low to control the system (too slow to adapt) and if it is too high, then the system will become too sensitive or unstable (if the control signal is too high). Once the magnitude of the learning rate is achieved, then it will be trivial to adjust it further. For example, if alpha and beta values set to 0.0001 is too low and 1 is too high, then around 0.01 will give the most appropriate response. It can then be increased or decreased based on the system response. No other parameters require any tuning and they can all be set to the default values as mentioned in the article.

This letter addresses the problem of controlling an autonomous agent to localize and circumnavigate a stationary or slowly moving target in the presence of an unknown wind disturbance. First, we introduce a novel wind estimator that utilizes bearing-only measurements to adaptively estimate the wind velocity. Then, we develop an estimator-coupled circumnavigation controller to mitigate wind effects, enabling the agent to move in a circular orbit centered at the target with a predefined radius. We analytically prove that the estimation and control errors are locally exponentially convergent when the wind is constant and the target is stationary. Then, the robustness of the method is evaluated for a slowly moving target and time-varying wind. It is shown that the circumnavigation errors converge to small neighborhoods of the origin whose sizes depend on the velocities of the wind and that of the target. Comprehensive simulation and experiments using unmanned aerial vehicles (UAVs) illustrate the efficacy of the proposed estimator and controller.

Aiming at applying unmanned aerial vehicle (UAV) emergency transportation in marine scene, this paper investigates an adaptive neural network fault-tolerant control strategy for quadrotor-slung-load system (QSLS), in which actuator faults, marine wind disturbance and suspended payload are considered simultaneously. Firstly, dynamics model of QSLS and detailed marine wind field are established. Then, a fault-tolerant controller is designed by combining disturbance observer (DO) and radial basis function (RBF) neural network, in which external disturbances as well as lumped internal disturbances including system uncertainties and actuator faults are effectively inhibited. In addition, gradient descent algorithm (GDA) is introduced to train RBF to better approximate unknown dynamics and compensate the system. And Lyapunov stability analysis demonstrates convergence of the proposed strategy. Finally, the effectiveness and superiority of the proposed scheme are fully verified by comparative simulation and experimental tests.

No abstract available

In this article, the aerial load transportation of an unknown slung payload in a windy environment is investigated. First, a full dynamics of the load and quadrotor is derived through the Euler-Lagrange method where the rotational and transitional drag forces are considered. Then, a robust sliding mode control is designed for the transitional movements to cope with both disturbances such as the wind forces and payload swings, and uncertainties such as the length of the cable and the mass of the load. In addition, the PD control method is presented for the quadrotor attitude control. Finally, numerical results show that the appropriate performance of the controllers during the transporting mission despite disturbances. Moreover, the Dryden wind model is utilized to model the real condition of wind velocity.

This paper addresses the robust control for a slung-mass quadrotor under abrupt velocity changes. The proposed algorithm is based on a sliding mode controller applied to the quadrotor translational dynamics considering the slung-mass angle as feedback. A Lyapunov candidate function is used to demonstrate system stability. Numerical simulations are performed to demonstrate stability in hover, forward flight, and under abrupt velocity changes. Experimental tests show that the proposed approach is also robust for stabilizing the aerial vehicle against disturbances caused by slung-load oscillations and wind gusts in outdoor environments by stopping the forward velocity from 29 km/h to hover by compensating within 1 s for the slung-mass oscillations.

Accurate dynamics modeling is essential for quadrotors to achieve precise trajectory tracking in various applications. Traditional physical knowledge-driven modeling methods face substantial limitations in unknown environments characterized by variable payloads, wind disturbances, and external perturbations. On the other hand, data-driven modeling methods suffer from poor generalization when handling outof-distribution (OoD) data, restricting their effectiveness in unknown scenarios. To address these challenges, we introduce the Physics-Informed Wind-Adaptive Network (PI-WAN), which combines knowledge-driven and data-driven modeling methods by embedding physical constraints directly into the training process for robust quadrotor dynamics learning. Specifically, PI-WAN employs a Temporal Convolutional Network (TCN) architecture that efficiently captures temporal dependencies from historical flight data, while a physics-informed loss function applies physical principles to improve model generalization and robustness across previously unseen conditions. By incorporating real-time prediction results into a model predictive control (MPC) framework, we achieve improvements in closed-loop tracking performance. Comprehensive simulations and real-world flight experiments demonstrate that our approach outperforms baseline methods in terms of prediction accuracy, tracking precision, and robustness to unknown environments.

This paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications.

No abstract available

No abstract available

One of the applications of unmanned aerial vehicles (UAVs) is to transport various cargoes such as medical supplies, food, and electronic devices. Hence, a significant challenge in cargo transportation is mitigating disturbances along the trajectory, including external factors like wind and uncertainties affecting the system model. This study introduces a robust adaptive integral fast terminal sliding mode control strategy (AIFTSMC) for an uncertain quadrotor UAV carrying a load, faced with adverse external influences. The system’s integrated model is derived using the Lagrange–Euler method for the quadrotor’s translational subsystem and the load, and the Newton–Euler method for the rotational subsystem. Nonlinear and robust control methods exhibit promising potential for guiding and stabilizing this intricate and underactuated system, with sliding mode control (SMC) techniques standing out for their distinctive attributes. Designing the IFTSMC technique enables trajectory tracking and load management to reduce steady-state errors within a finite-time frame. By incorporating adaptive rules with upper-bound estimates for undesirable factors, controller consistency is enhanced. The proposed controller demonstrates robust performance within a finite time. Simulation comparisons with existing methods, sensitivity analyses to various initial conditions and load angles, unequivocally demonstrate the superior efficacy of the proposed control strategy over alternative approaches.

This article proposes an actuator fault detection and isolation (FDI) scheme for quadrotor unmanned aerial vehicle (UAV) with a cable-suspended load. First, a linear parameter-varying (LPV) model of quadrotor UAV is established, in which the effects of cable-suspended load are considered. Then, a state boundary-based FDI design is systemically presented. A bank of interval observers is constructed to build the preliminary upper and lower boundaries of system states under healthy conditions, where $H_{-}/H_{\infty }$ performance is applied to enhance its robustness against disturbances and sensitivity to faults. Furthermore, a novel updating strategy is further proposed to periodically adjust state boundaries to cope with the effects of varying wind disturbances. Finally, based on the QDrone platform, experimental tests under random faults are carried out to verify the effectiveness and performance of the proposed scheme.

The extensive deployment of quadrotors in complex environmental missions has revealed a critical challenge: degradation of trajectory tracking accuracy due to time-varying wind disturbances. Conventional model-based controllers struggle to adapt to nonlinear wind field dynamics, while data-driven approaches often suffer from catastrophic forgetting that compromises environmental adaptability. This paper proposes a reinforcement learning framework with continual adaptation capabilities to enhance robust tracking performance for quadrotors operating in dynamic wind fields. We develop a continual reinforcement learning framework integrating continual backpropagation algorithms with reinforcement learning. Initially, a foundation model is trained in wind-free conditions. When wind disturbance intensity undergoes gradual variations, a neuron utility assessment mechanism dynamically resets inefficient neurons to maintain network plasticity. Concurrently, a multi-objective reward function is designed to improve both training precision and efficiency. The Gazebo/PX4 simulation platform was utilized to validate the wind disturbance stepwise growth and stochastic variations. This approach demonstrated a reduction in the root mean square error of trajectory tracking when compared to the standard PPO algorithm. The proposed framework resolves the plasticity loss problem in deep reinforcement learning through structured neuron resetting, significantly enhancing the continual adaptation capabilities of quadrotors in dynamic wind fields.

Unmanned aerial vehicles serve as carriers for diverse payloads, emphasizing the importance of achieving load transportation with trajectory-smoothing capabilities to ensure safety. Addressing the challenge of payload transport in varying environments involves coping with external disturbances like wind and uncertainties linked to the payload. This paper introduces a control strategy consisting of two loops. The outer loop employs robust backstepping control to attain Euler angles and control laws. Meanwhile, the inner loop utilizes a controller combining backstepping and fast terminal sliding mode control to regulate the yaw angle and tilting angles. The research adopts a comprehensive modeling approach, using the Lagrange-Euler method for the translational subsystem of the quadrotor and dynamic modeling for the suspended load, alongside the Newton-Euler method for the rotational subsystem. The aim is to identify a suitable control strategy that ensures practical guidance and stability for the entire system. The proposed method exhibits robust performance in tracking desired trajectories and managing load perturbations within a finite time. Lastly, simulation results are presented, comparing the proposed control method with other approaches. The simulations demonstrate the superior performance of the proposed control method over other techniques

There are diverse applications in industry, agriculture and logistics which can benefit from the use of quadrotor Uncrewed Aerial Vehicles (UAVs) with cable-suspended payloads. Their control is a challenging topic, given the nonlinear and underactuated dynamics of the UAV and the oscillations of the cargo. In this work, we propose a chattering-free sliding mode controller (SMC) for position and attitude tracking of a quadrotor with a cable-suspended payload. Firstly, the model of the UAV and the load is derived via Lagrangian approach in matrix form. Then, the system is divided into two subsystems and four sliding mode controllers are designed. The chattering effect is eliminated via approximation of the signum function with a saturation function. Moreover, our approach permits computation of the sliding surface coefficients using a simple linearization of the dynamic model and Hurwitz analysis. The stability of the closed-loop controller is proven via Lyapunov theory and its effectiveness is validated through simulations. The proposed SMC allows to track the desired references when a quadrotor of 1.1 kg performs non-aggressive maneuvers, transporting a payload of 0.2 kg with a cable of 1 m. In this scenario, our approach ensures that the oscillations of the load are always smaller than 4.6 degrees.

This paper investigates optimal takeoff trajectory planning for a quadrotor modeled with vertical-plane rigid body dynamics in an uncertain, one-dimensional wind-field. The wind-field varies horizontally and propagates across an operating region with a known fixed speed. The operating area of the quadrotor is equipped with wind-sensing infrastructure that shares noisy anemometer measurements with a centralized trajectory planner. The measurements are assimilated via Gaussian process regression to predict the wind at unsampled locations and future time instants. A minimum-time optimal control problem is formulated for the quadrotor to take off and reach a desired vertical-plane position in the presence of the predicted wind-field. The problem is solved using numerical optimal control. Several examples illustrate and compare the performance of the trajectory planner under varying wind conditions and sensing characteristics.

This paper presents a software pipeline that enables simulating a quadrotor’s flight in realistic urban wind fields where complex wind phenomena are common and have significant impact on vehicle dynamics. The pipeline integrates the OpenStreetMap database for obtaining real-world building geometry, the OpenFOAM computational fluid dynamics (CFD) solver for computing a three-dimensional, steady, wind field, the Gazebo robotics simulation environment, and the PX4 software-in-the-loop autopilot. A 3D wind plugin is developed to interpolate a pre-computed CFD wind field at runtime during the simulation. The approach is demonstrated by comparing the flight performance of a quadrotor flying over a university campus environment with and without the wind field.

Vision-based interception using multicopters equipped strapdown camera is challenging due to camera-motion coupling and evasive targets. This article proposes a method integrating image-based visual servoing (IBVS) with proportional navigation guidance (PNG), reducing the multicopter’s overload in the final interception phase. It combines smoother trajectories from the IBVS controller with high-frequency target 2-D position estimation via a delayed Kalman filter (DKF) to minimize the impact of image processing delays on accuracy. In addition, a field-of-view (FOV) holding controller is designed for the stability of the visual servo system. Experimental results show a circular error probability (CEP) of 0.089 m (72.8% lower than the latest relevant IBVS work) in simulations and over 80% interception success under wind conditions below 4 m/s in real world. These results demonstrate the system’s potential for precise low-altitude interception of noncooperative targets.

Urban air mobility (UAM) aircraft operate near the planetary surface, exposing them to complex wind conditions in the atmospheric turbulence layer. The interaction of atmospheric turbulence with the aircraft significantly impacts its structure as well as its aerodynamic and aeroacoustic performance. This study employs an efficient mid-fidelity aerodynamic method, integrated with the Ffowcs Williams–Hawkings (FW–H) acoustic analogy and a stochastic full-field inflow turbulence generator to investigate the aerodynamic and acoustic performance of side-by-side UAM aircraft while cruising through turbulent inflow. Under uniform wind conditions, aerodynamic loads exhibit steady variations once the rotor wake reaches a converged state. However, as the severity of turbulence increases, the wake structures become increasingly disrupted and eventually deteriorate significantly. Turbulent inflow leads to increased unsteadiness and blade–vortex interactions (BVI), altering both aerodynamic and acoustic characteristics. Higher turbulence levels result in elevated sound pressure levels and alterations of acoustic patterns. The impact of inflow turbulence on noise emission is less pronounced in the aft region of the UAM aircraft compared to other directions due to the dominant wake structure propagating downward, mitigating the influence of atmospheric turbulence in that region.

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview’s timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts.

No abstract available

In this letter, an energy-efficient algorithm for positioning of unmanned aerial vehicle-based base stations (UAV-BSs) is presented. The objective is to reduce the propulsion power consumption of UAV-BSs while not compromising the communication capacity of user equipments (UEs). As a significant step beyond state-of-the-art, we consider an effect of wind. To this end, we develop a new model of a propulsion energy consumption for the UAV-BSs reflecting an impact of wind. Furthermore, we propose a novel algorithm based on an ensemble learning optimizing the 3D trajectory of UAV-BSs over time in realistic environment with wind to reduce the propulsion energy consumption. The results show that the proposed approach reduces the propulsion energy consumption of UAV-BSs by up to 47% with only a negligible degradation in the UEs capacity compared to state-of-the-art works.

UAVs are susceptible to turbulence when flying, which can alter the expected navigational attitude, flight trajectory and other states and affect the reasonable execution of the mission, and gust mitigation technology is a powerful means for UAVs to cope with the effects of gusts. This paper designs a gust mitigation measurement and control system for UAVs using the STM32H743 as the control core. The system consists of a master control unit, a data acquisition module, a serial module, a rudder control module and a data storage module. When the UAV is affected by gusts of wind, the system acquires the current flight status of the UAV by accurately collecting data from the UAV sensors, and then combines the control commands from the ground station and the flight control to fuse and solve the data, and then generates rudder control commands to control the rudder deflection, so that the UAV can maintain stable flight and achieve gust mitigation. After actual circuit fabrication and experimental testing, the measurement and control system designed in this paper can achieve stable serial communication, accurate analogue signal acquisition, precise motor and rudder control and stable power supply, meeting all the indicators and requirements required to achieve gust mitigation.

The attitude change of unmanned aerial vehicles (UAVs) caused by wind fields may lead to beam misalignment problems in millimeter wave (mmWave) UAV networks, which would reduce the transmission spectral efficiency (SE) significantly. In this paper, we propose a novel attitude compensation-based beam tracking (ACBT) method, which enables UAVs to maintain beam alignment despite attitude changes. Simulation results demonstrate that, by adjusting the beam direction dynamically, the proposed ACBT method can maintain the stability and efficiency of mmWave links in UAV networks, thus enhancing the overall performance of aerial communications.

3D phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available UAV captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes. We will publicly release the source code of our reconstruction pipeline. Additionally, we provide a dataset consisting of multiple plants from various crops, captured across different points in time.

This article presents a real-time trajectory optimizer for shore-to-ship operations using Unmanned Aerial Vehicles (UAVs). This concept aims to improve the efficiency of the transportation system by using UAVs to carry out parcel deliveries to offshore ships. During these operations, UAVs would fly relatively close to manned vessels, posing significant risks to the crew in the event of any incident. Additionally, in these areas, UAVs are exposed to meteorological phenomena such as wind gusts, which may compromise the stability of the flight and lead to potential collisions. Furthermore, this is a phenomenon difficult to predict, which poses a risk that must be considered in the operations. For these reasons, this work proposes a gust-aware multi-objective optimization solution for calculating fast and safe trajectories, considering the risk of flying in areas prone to the formation of intense gusts. Moreover, the system establishes a risk buffer with respect to all vessels to ensure compliance with EASA (European Union Aviation Safety Agency) regulations. For this purpose, Automatic Identification System (AIS) data are used to determine the position and velocity of the different vessels, and trajectory calculations are periodically updated based on their motion. The system computes the minimum-cost trajectory between the ground base and a moving destination ship while keeping these risk buffer constraints. The problem was solved through an Optimal Control formulation discretized on a dynamic graph with time-dependent costs and constraints. The solution was obtained using a Reaching Method that allowed efficient and real-time computations.

This paper presents a high-gain extended observer-based adaptive sliding mode scheme for a quadrotor rotorcraft influenced by turbulent wind gusts while performing a defined trajectory. The drag forces caused by the turbulent wind field are estimated through an extended state which collects all uncertainties and perturbations affecting the system. Such estimation is then compensated in the feedback control loop performed by a class of adaptive sliding mode control which provides properties such as robustness, non-overestimation of the control gain, and finite-time convergence. Computer simulation results corroborate the successful performance and feasibility of the proposed strategy even under output noise circumstances.

The multirotor unmanned aerial vehicles (UAVs) are currently commonly used for various outdoor operations; However, gusts of wind may affect their stable control, and even lead to safety accidents. The common approach is to install wind sensors to obtain wind information for gust-resistant control, but traditional sensors are large, heavy, and slow to respond, making them unsuitable for small UAVs. In this paper, a compact, lightweight, fast-responsive wind estimation module is designed. When wind passes over the module, the surface pressure distribution information will be captured by the pressure sensors, and an artificial neural network is proposed to estimate the wind speed and direction. The effectiveness of the module was verified by testing it on a small UAV in the 5m/s wind field, achieving 4.09° direction estimation error and 0.34m/s speed estimation error.

In the field of aviation, safety is a critical cornerstone, and the operation of the Unmanned Aerial Vehicle (UAV) systems is deeply connected with this principle. A thorough analysis and rigorous simulation and testing of aircraft systems are essential to avoid severe safety hazards. This paper delves into the safety issue in UAV operations, specifically regarding maintaining minimum safety distances under fluctuating wind conditions. The study introduces a novel solution based on a Deep Deterministic Policy Gradient (DDPG) model, a reinforcement learning method. The DDPG model was trained using a simulated environment created through the Gazebo simulator, with values for wind and gust conditions derived from historical records at the KLAF airport at Purdue University. The model’s performance was evaluated regarding maintaining safe distances under these conditions. The results indicate that the DDPG model can accurately predict safety distances with relatively low error rates when predicting under different weather conditions. The findings significantly contribute to UAV safety operations, suggesting the potential future utilization of reinforcement learning methods to study enhancing airspace efficiency and obstruction avoidance in UAVs.

For addressing the challenges of decreased attitude and trajectory tracking accuracy and a delayed response in the flight control operations of quadcopter unmanned aerial vehicles (UAVs) under the uncertainties of model parameters and external disturbances, this study leverages the advantages of the non-causal declarative modeling language Modelica in system modeling and simulation. In addition, it incorporates the nonlinear Active Disturbance Rejection Control (ADRC) framework for disturbance observation, estimation, and compensation. A state observer is designed to mitigate the impact of external disturbances and model uncertainties through feed-forward compensation, and stability analysis is conducted. Numerical simulations for hover resistance demonstrate that, compared to the cascade proportional integral differential (PID) control strategy, PID-NLADRC reduces the maximum deviation induced by wind disturbances by ∼50% and shortens the disturbance influence time by around 40%. Simulations for different trajectories, such as planar or spatial, smooth or abrupt changes, indicate that under the PID-NLADRC control strategy, the real-time spatial distance deviation mean is reduced by 69.5%, and the peak time is shortened by 75.7%. Validation through multi-objective applications and physical experiments highlights the advantages of PID-NLADRC in terms of positioning accuracy, rapid tracking, and disturbance suppression, aligning well with the fast, precise, and robust flight control requirements of quadcopter UAVs.

This article presents an active wind rejection control scheme for a quadrotor unmanned aerial vehicle (UAV) against unknown winds. Based on the estimated wind effects acting on the aircraft, the proposed control scheme can maintain the performance of the quadrotor UAV in the presence of model uncertainties, unknown winds, and system noises. Firstly, a two-stage particle filter is designed to estimate the UAV states and wind information from the motion of the vehicle without additional wind sensors. Then, an active wind rejection control scheme is proposed to actively attenuate the wind disturbances based on the estimated wind information. In the controller design, the nonsingular terminal sliding-mode control (NTSMC) is chosen as the baseline controller. To tackle the issues of model uncertainties and wind estimation errors, the adaptive drag coefficients are adopted to generate the compensation control signals. Finally, simulation results are presented to demonstrate the effectiveness of the proposed active wind rejection control scheme for a quadrotor UAV against unknown winds.

The paper presents a resilient dynamic adaptive event-triggered sliding mode control (DAET–SMC) framework for fractional-order delayed multi-agent systems under actuator saturation, stochastic disturbances, and cyber-attacks. Existing methods often fail to ensure containment and formation stability when multiple practical constraints coexist. The proposed approach leverages Riemann–Liouville fractional dynamics to capture system memory effects and integrates adaptive compensation to mitigate actuator faults, measurement attacks, and communication delays. Numerical simulations on a 16-agent network with one leader and fifteen followers show that all followers achieve containment within 20 s, with formation errors below 10−2m, while maintaining bounded control effort. Compared with conventional non-adaptive controllers, the proposed method demonstrates faster convergence, superior robustness, and resilience under combined disturbances, achieving up to 35% faster error convergence and maintaining control input within saturation limits. These results confirm the effectiveness of the DAET–SMC strategy for practical multi-agent coordination in uncertain and constrained environments.OPEN ACCESS Received: 30/10/2025 Accepted: 26/11/2025 Published: 23/01/2026

This article describes and experimentally evaluates a comprehensive system identification framework for high‐performance UAV control in wind. The framework incorporates both linear offline and nonlinear online methods to estimate model parameters in support of a nonlinear model‐based control implementation. Inertial parameters of the UAV are estimated using a frequency‐domain linear system identification program by incorporating control data obtained from motor‐speed sensing along with state estimates from an automated frequency sweep maneuver. The drag‐force coefficients and external wind are estimated recursively in flight with a square‐root unscented Kalman filter. A custom flight controller is developed to handle the computational demand of the online estimation and control. Flight experiments illustrate the nonlinear controller's tracking performance and enhanced gust rejection capability.

Quadrotor UAVs have extensive low-altitude mission capabilities, but their low-altitude and low-speed flight envelope makes them more susceptible to wind disturbance. Therefore, the 6-DOF dynamic model of UAV with wind disturbance is established, which can effectively reflect the influence of wind velocity gradient on dynamics. Meanwhile, wind velocity gradient models of three major atmospheric disturbances, namely turbulence, windshear and gust, are given. On this basis, the flight state changes of UAV under the three atmospheric disturbances are simulated and analysed. Through comparison, it is clear that the vertical variation of windshear and continuous vertical turbulence are the main wind threats affecting UAV flight safety.

In city-wide weather prediction, wind gust information can be obtained using unmanned aerial vehicles (UAVs). Although wind sensors are available, an algorithm-based active estimation can be helpful not only as a weightless substitute but also as feedback for robust control. This paper aims to estimate the wind gusts affecting the quadrotors (a type of UAV) as the input disturbances by using a frequency-based nonlinear disturbance observer (NDOB). To obtain highly accurate estimations, frequency is considered as the main design parameter, thereby focusing the estimation on the frequency range of the wind gusts. The NDOB is developed using the Takagi-Sugeno (T-S) fuzzy framework. In this approach, the twelfth-order nonlinear model is approximated into a sixth-order T-S fuzzy model to reduce computational cost. A two-step verification method is presented, which includes MATLAB/Simulink simulations and the experiments performed using a 2.5 kg quadrotor.

In order to accomplish high-precision trajectory tracking of quadrotor Unmanned Aerial Vehicle (UAV), it was susceptible to wind disturbance. An adaptive sliding-mode linear active disturbance rejection control (ASMC-LADRC) algorithm was presented under wind disturbance. The double closed-loop structure of inner-loop and outer-loop control would be adopted as the overall system designs. An adaptive sliding-mode control(ASMC) was designed for the outer-loop to track the trajectory and generate the desired lift force and attitude. It was proposed that a third-order linear active disturbance rejection control(LADRC) based on free-forward compensation as the inner-loop followed the desired attitude.The simulation and experiment results show that the designed algorithm can make quadrotor UAV track trajectories effectively and have strong capacity of resisting interference under wind disturbance.

Infrastructure inspections using UAVs have surged in recent years thanks to their ability to capture high-resolution imagery in hard-to-reach areas. Their versatility has garnered significant interest in applications such as bridge inspections, offering the potential to substantially reduce both costs and inspection time. However, UAVs are highly sensitive to environmental factors like turbulence and wind gusts, which can com-promise their stability and lead to accidents. This is particularly critical in bridge inspections, where structural components such as pillars, decks, and cables generate complex wind patterns, including vortices and turbulence. To address these challenges, this paper presents a wind assessment methodology for UAV-based bridge inspections. To this end, an automated 3D urban geometry modeling methodology was developed using open-source geospatial data, and wind predictions were calculated via the CFD (Computational Fluid Dynamics) software Open-FOAM. A practical case study was carried out in Porto, Portugal, to validate the proposed methodology.

ABSTRACT In recent years, the use of Unmanned Aerial Vehicles (UAVs) for remote sensing and aerial photogrammetry has surged, owing to their affordability and ability to capture high-resolution data in hard-to-reach areas. However, the effectiveness of these platforms can be limited by environmental factors such as turbulence and wind gusts, which may destabilize the aircraft and compromise the proper exposure of images. To address this issue, this work presents a terrain survey planner that considers the environmental conditions derived from a meteorological forecast. The system employs Computational Fluid Dynamics (CFD) simulations to generate high-resolution wind predictions for a given study area. This data is integrated into a state-of-the-art UAV simulator to estimate aircraft behaviour at various locations. Additionally, it incorporates an empirically calibrated camera model to predict sensor performance based on solar radiation estimates. With this information, a multi-objective optimization is performed, computing the optimal path and camera settings to mitigate the impact of wind on photogrammetry. Results highlight the significant impact of wind and poor lighting on motion blur, emphasizing the need to carefully plan not only the inspection path but also the time and date for correct image exposure.

Unmanned aerial vehicles (UAVs) are finding use in applications that place increasing emphasis on robustness to external disturbances including extreme wind. However, traditional multirotor UAV platforms do not directly sense wind; conventional flow sensors are too slow, insensitive, or bulky for widespread integration on UAVs. Instead, drones typically observe the effects of wind indirectly through accumulated errors in position or trajectory tracking. In this work, we integrate a novel flow sensor based on micro-electro-mechanical systems (MEMS) hot-wire technology developed in our prior work [1] onto a multirotor UAV for wind estimation. Our sensor is omnidirectional (in the plane), lightweight, fast, and accurate. In order to achieve superior hover performance in windy conditions, we train a ‘wind-aware’ residual-based controller via reinforcement learning using simulated wind gusts and their aerodynamic effects on the drone. In extensive hardware experiments, we demonstrate the wind-aware controller out-performing two strong ‘wind-unaware’ baseline controllers in challenging windy conditions. See: youtu.be/KWqkH9Z-338.

In this paper, an assessment of the state of coastal territories of Ecuador monitoring issue is conducted. The use of an autonomous robotic aerial platform is proposed as a technical solution to enhance the efficiency of remote surveillance missions performed by national security services along coastline. Considering the UAV nonlinear flight dynamics, as well as the missing information of the environment, is designed a UAV hierarchical control structure composed of an adaptive PID based MPC control strategy. The implementation of an adaptive PID based MPC controller leads to significantly improve the UAV optimal trajectory tracking task, as well as satisfy properties such as adaptiveness, self-learning, and capability of handling uncertainties caused by the unpredictable behavior of sea currents and wind loads retaining robust performance features. In this work, the investigation of external disturbances on UAV stabilization and positioning accuracy considers swirling wind flows and short-term wind gusts. These correspond to deterministic and random processes, are mathematically represented as trigonometric functions with random amplitudes determined by the gust coefficients and the wind loading periods of the pulses. The established range is given by a set of several observations of wind loads in the coastal territories of Ecuador. The analyzed data is collected from the database of national meteorological stations. Finally, the simulation process of the perturbed controlled motion of the UAV along a segmented linear trajectory, as well as the data analysis and graphics are carried out in the MATLAB environment.

This paper assesses the use of a ground-based wind measuring LiDAR (Light Detection and Ranging) for remote sensing of incoming wind gusts at the landing site of an autonomous quadrotor. The experimental verification results show that the scalar measurements from the LiDAR were able to recreate the horizontal wind vector even with wind direction variation. Comparisons were conducted against conventional cup anemometers with wind vanes, and these show a good correlation. Upwind LiDAR measurements were used to predict the downstream wind using a transport model. This prediction compared with the downwind measurement shows a good correlation. This wind preview information from the LiDAR is then incorporated into a disturbance feedforward control scheme to increase the gust resilience of the vehicle. Simulation and experimental results demonstrate the system's efficacy.

To study the influence of compound wind field disturbance on the stability of multi-rotor UAV(unmanned aerial vehicle), a dynamics modeling and simulation method for multi-rotor UAV under the compound wind field model is proposed. Firstly, based on the idea of equivalent modeling in practical engineering, the models of uniform wind, gust, turbulence and wind shear are constructed respectively. The numerical analysis and simulation research are then carried out. Second, the dynamic model of multi rotor UAV under the action of composite wind field is deduced, and the attitude control model is derived. Finally, the numerical analysis software is utilized to simulate and analyze the attitude response of the multi-rotor UAV under the condition of wind and no wind, and analyze its control performance under the interference of compound wind field. The simulation results show that the dynamic model of the multi-rotor UAV under the influence of the wind field can accurately reflect its dynamic performance under the action of the wind field, and the output attitude angle fluctuation is less than 2°. The obtained results show that the established wind field model can be effectively applied to the research on the flight performance of multi-rotor UAV under the interference of compound wind field.

Aimed at the problem that the unmanned aerial vehicle (UAV) close formation cannot maintain its desired formation shape due to the wind field disturbances, a novel distributed adaptive control approach is proposed to counteract the lateral and forward distance errors caused by uncertain wind field disturbances. Firstly, based on the “leader-follower” model, an adaptive control law is designed to accurately estimate the magnitude and direction of the wind in 3-D space. Then, the distance errors caused by uncertain wind field disturbances is counteracted by controlling UAV relative movement. Moreover, their velocity can keep constant to achieve the desired formation shape. Additionally, the signals of the closed-loop system are semi-globally practical finite-time stable (SGPFS). Finally, the simulation results show that the proposed adaptive control algorithm has good robustness, which provides a theoretical basis for engineering practice.

Quadrotor unmanned aerial vehicle (UAV) have always been a difficult problem in the design of UAV controllers in the face of complex exogenous disturbances during autonomous flight. Focusing on this problem, this paper put forward a sliding mode control method which combined with radial basis function neural network (RBFNN). Firstly, based on the Newtonian Euler equation, the trajectory tracking problem of quadrotor UAV is transformed into altitude and attitude control problem, and then the corresponding dynamic equations are obtained. Secondly, the controllers are designed based on the second-order sliding mode manifold, in the meantime, the gusts and uncertain parameters in the control law are estimated by using RBFNN. Simulation results show the stability and performance advantages of the proposed method applied to UAV control system.

No abstract available

Quadrocopter UAV is widely used. It is of great significance to study the control of its motion attitude under the action of external wind field. First innovation, this paper builds the basic wind, gust and gradient wind and random wind of composite wind field model, can be very good to the outside world most natural wind simulation, and then combined in the vehicle system dynamics equations of four uav rotorcraft in hover and geodetic coordinate system of the four rotor dynamics equation of unmanned aerial vehicle, build the feedback mechanism model of unmanned aerial vehicle quantified self-adjusting. By analyzing the variation of the unstable wind and the movement state of the quadrotor UAV, the maximum wind speed that the UAV can keep hovering state under the compound wind field can be obtained. The simulation results show that when the wind speed variation factor is 0.2, 0.5 and 0.8, the maximum wind speed that the quadrotor UAV can tolerate is 5.474m/s, 5.300m/s and 5.564m/s, respectively. The research in this paper can provide reference for the attitude adjustment and attitude control of the quadrotor UAV in the wind field. Through theoretical calculation of the model in this paper, the maximum wind speed that the quadrotor UAV can maintain stability in the unstable wind field can be obtained.

In this paper, a control strategy based on a coupled Proportional and State-Dependent Riccati Equation (P-SDRE) controller, which ensures a quick stabilization for a given angular position of an unmanned aerial vehicle in three-dimensional space, is presented. The Hamilton-Jacobi-Bellman function and the Riccati differential equation are used to develop the solution of the SDRE algorithm. Properly defined weighting matrices in performance index allow quadrotor’s fast and precise positioning with optimal stabilization of angular speeds. Cruise control of unmanned aerial vehicle is modelled and simulated. In order to prove the effectiveness of the method, disturbances modelled as wind gusts are included in the simulation.