Model Of Unmanned Aerial Vehicle Research Articles

Optimal Periodic Control of Unmanned Aerial Vehicles Based on Fourier Integral Pseudospectral and Edge-Detection Methods

The endurance of unmanned aerial vehicles (UAVs) is a critical factor in expanding the scope of their applications. Extended flight times enable UAVs to undertake longer missions, cover larger areas and perform tasks such as persistent surveillance, data collection and search and rescue operations. Optimal trajectory planning is a cost-effective method to significantly enhance UAV endurance and performance by minimizing fuel consumption. This study introduces a novel numerical optimization framework to maximize UAV endurance. Specifically, we address the problem of determining optimal thrust and cruise angle of attack for a UAV in 2D space under specific initial, periodic and boundary conditions. By normalizing the free final time optimal control problem and employing Fourier collocation and quadrature, we transform it into a nonlinear programming problem. A key contribution of this work is accurately detecting and reconstructing the thrust history, including jump discontinuities, directly from Fourier pseudospectral data without smoothing techniques. The proposed method outperforms existing approaches in solving the periodic energy-optimal path planning problem for UAVs, as it effectively reconstructs the bang-bang thrust profile, facilitating rapid and efficient thrust adjustments essential for various flight maneuvers. Furthermore, the algorithm aligns with the UAV model, ensuring seamless integration into real-world control systems. The method’s independence from prediction horizon length, due to the use of Fourier collocation on the normalized interval [Formula: see text], is a notable advantage. This characteristic offers potential for future applications in various fields involving nonsmooth optimal control problems. This research generally provides a valuable tool for researchers and engineers working on UAV design and operation, paving the way for more efficient and effective UAV systems.

A High Performance Nonlinear Longitudinal Controller for Fixed-Wing UAVs Based on Fuzzy-Guaranteed Cost Control

Unmanned aerial vehicles (UAVs) have garnered more attention across various industries in recent years, leading to significant development in their design and application globally. Due to the high coupling between UAV states, model uncertainties, and various disturbances, precise longitudinal control of UAVs remains a significant research challenge. Oriented for the speed and altitude control of an electrical-powered fixed-wing UAV, this paper introduces a new control strategy based on the fuzzy guaranteed cost control (F-GCC) technique, which results in a nonlinear longitudinal state feedback control law with strict stability criterion, effectively addressing the issue of state coupling. Moreover, the strategy also includes a thrust estimation model of the electrical propulsion system to significantly reduce the nonlinearity, simplifying the controller design while effectively preserving tracking control performance. Through numerical validation, the UAV longitudinal nonlinear controller designed using the F-GCC technique offers better transient response and stronger robustness than the traditional linear and ADRC controllers.

Rapid Optimization of Active Disturbance Rejection Controller Parameters for Quadrotor UAVs Using Kriging Surrogate Modeling

The Active Disturbance Rejection Controller (ADRC), celebrated for its superior resistance to interference, presents itself as an exemplary solution for the development of control systems that are designed to accommodate substantial changes in payload weight for quadrotor Unmanned Aerial Vehicles (UAVs) and to endure robust side winds along with other challenging operational scenarios. Despite the inherent complexity due to the numerous parameters required for the configuration of the ADRC, an innovative method utilizing the Kriging surrogate optimization algorithm has been introduced to automate and expedite the generation of these controller parameters. The development of the ADRC begins with the dynamics model of the quadrotor UAV, followed by the identification of key design parameters. These parameters are then rapidly optimized through the Kriging surrogate optimization algorithm. The controller’s effectiveness is confirmed by implementing the ADRC on Pixhawk flight control hardware, with a comparative analysis of the attitude response under various operating conditions, thereby validating the ADRC’s superior anti-disturbance performance.

Evade Unknown Pursuer via Pursuit Strategy Identification and Model Reference Policy Adaptation (MRPA) Algorithm

The game of pursuit–evasion has always been a popular research subject in the field of Unmanned Aerial Vehicles (UAVs). Current evasion decision making based on reinforcement learning is generally trained only for specific pursuers, and it has limited performance for evading unknown pursuers and exhibits poor generalizability. To enhance the ability of an evasion policy learned by reinforcement learning (RL) to evade unknown pursuers, this paper proposes a pursuit UAV attitude estimation and pursuit strategy identification method and a Model Reference Policy Adaptation (MRPA) algorithm. Firstly, this paper constructs a Markov decision model for the pursuit–evasion game of UAVs that includes the pursuer’s attitude and trains an evasion policy for a specific pursuit strategy using the Soft Actor–Critic (SAC) algorithm. Secondly, this paper establishes a novel relative motion model of UAVs in pursuit–evasion games under the assumption that proportional guidance is used as the pursuit strategy, based on which the pursuit UAV attitude estimation and pursuit strategy identification algorithm is proposed to provide adequate information for decision making and policy adaptation. Furthermore, a Model Reference Policy Adaptation (MRPA) algorithm is presented to improve the generalizability of the evasion policy trained by RL in certain environments. Finally, various numerical simulations imply the precision of pursuit UAV attitude estimation and the accuracy of pursuit strategy identification. Also, the ablation experiment verifies that the MRPA algorithm can effectively enhance the performance of the evasion policy to deal with unknown pursuers.

Aerodynamic Analysis of Rotor Spacing and Attitude Transition in Tilt-Powered Coaxial Rotor UAV

Complex aerodynamic characteristics and optimal control during the attitude transition of tilt-powered coaxial twin-rotor unmanned aerial vehicles (UAVs) represent key challenges in flight control design. This study investigates aerodynamic mechanisms and control parameter optimization during the transition of UAVs from vertical to forward flight. By establishing a dynamic model and combining theoretical and numerical analyses, the optimal rotor spacing is determined to be h = 0.5 R. The load distribution and aerodynamic characteristics of the aircraft are analyzed at different initial tilt angles during attitude transitions. At an initial tilt angle of δ = 9°, the thrust force increases by 439% compared with that at δ = 3°, and the tip speed increases by 15% and 35% compared with that at δ = 3° and δ = 13°, respectively. The results indicate that a tilt angle of δ = 9° results in a higher turbulent dissipation rate and rotor layout efficiency, with a smoother vortex flow and more orderly distribution. The interference between the twin-rotor tip vortices is relatively weak, resulting in excellent symmetry and aerodynamic stability. Through the improvement of the theoretical model and parameter optimization of a novel tilt-powered coaxial twin-rotor UAV, this study enhances UAV flight stability and provides valuable insights and validation for the further development of UAV technology.

LiDAR-Based Unmanned Aerial Vehicle Offshore Wind Blade Inspection and Modeling

The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine.

Event-triggered finite-time adaptive neural network control for quadrotor UAV with input saturation and tracking error constraints

In this article, an event-triggered finite-time adaptive neural network control tracking strategy is proposed for quadrotor unmanned aerial vehicle (UAV) with input saturation and error constraints. Firstly, the radial basis function neural networks (RBFNNs) are adopted to identify the unknown uncertainty of quadrotor UAV model from the installation errors, gyroscope errors and so on. An auxiliary equation is constructed to deal with input physical saturation from the actuator motors. Additionally, by combining the performance function and error transformation, the issue of error constraint is solved. Based on the Lyapunov stability theory and event-triggered mechanisms, a finite-time adaptive neural network scheme is developed to ensure that the closed-loop quadrotor UAV system is semi-globally practically finite-time stable, and save the computation, resources, and transmission load. Finally, the simulation results illustrate the good tracking performance of quadrotor UAV by using the proposed control strategy.

Motor Fault Detection and Isolation for Multi-Rotor UAVs Based on External Wrench Estimation and Recurrent Deep Neural Network

AbstractFast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing one or more rotors. We present a motor fault detection and isolation (FDI) method for multi-rotor UAVs based on an external wrench estimator and a recurrent neural network composed of long short-term memory nodes. The proposed approach considers the partial or total motor fault as an external disturbance acting on the UAV. Hence, the devised external wrench estimator trains the network to promptly understand whether the estimated wrench comes from a motor fault (also identifying the motor) or from unmodelled dynamics or external effects (i.e., wind, contacts, etc.). Training and testing have been performed in a simulation environment endowed with a physic engine, considering different UAV models operating under unknown external disturbances and unexpected motor faults. To further assess this approach’s effectiveness, we compare our method’s performance with a classical model-based technique. The collected results demonstrate the effectiveness of the proposed FDI approach.

Characteristics Analysis and Modeling of Integrated Sensing and Communication Channel for Unmanned Aerial Vehicle Communications

As an important part of 6th generation (6G) communication, integrated sensing and communication (ISAC) for unmanned aerial vehicle (UAV) communication has attracted more and more attention. The UAV ISAC channel model considering the space-time evolution of joint and shared clusters is the basis of UAV ISAC system design and network evaluation. This paper introduces the UAV ISAC channel characteristics analysis and modeling method. In the UAV ISAC network, the channel consists of a communication channel and a sensing channel. A joint channel parameter is a combination of all (communication and sensing) multiple path component (MPC) parameter sets, while a shared path is the intersection of the communication path and sensing path that have some of the same MPC parameters. Based on the data collected from a ray-tracing (RT) UAV-to-ground scenario, the joint paths and shared paths of ISAC channels are clustered. Then, by introducing the occurrence and disappearance of clusters based on the birth–death (B–D) process, the space-time evolution of different clusters is described, and the influence of the addition of sensing clusters and the change in flight altitude on the B–D process is explored. Finally, the effects of the sensing cluster and flight altitude on the UAV ISAC channel characteristics, including the angle, time–varying characteristics, and sharing degree (SD), are analyzed. The related UAV ISAC channel characteristics analysis can provide reference for the future development of UAV ISAC systems.

Computational fluid dynamics analysis of aerodynamic characteristics in long-endurance unmanned aerial vehicles

Long-endurance unmanned aerial vehicles (UAVs) play an increasingly important role in various aspects of societal life. In response to the national emphasis on air force development, a study on the aerodynamic characteristics of long-endurance UAVs was conducted. This paper utilizes SolidWorks software to construct a geometric model based on the MQ-9 UAV, and a CFD method to establish a simulation model for UAV cruising flight. The aerodynamic coefficients, required thrust, and total mass during UAV cruise were calculated, and the variation of aerodynamic coefficients as airflow passes through the UAV was described. A comparative analysis was performed on the effects of different angles of attack, flight speeds, and flight altitudes on aerodynamic efficiency. The study results indicate that at an altitude of 10 km, with a 0° angle of attack, the UAV achieves a lift coefficient of 0.8888, a drag coefficient of 0.0679, and a lift-to-drag ratio of 13.0988. The required thrust is approximately 2236 N, and the total flight mass is approximately 3090 Kg. The angle of attack has the greatest impact on aerodynamic efficiency, followed by flight altitude, while flight speed has the least impact on aerodynamic performance. The lift-to-drag ratio initially increases sharply and then decreases rapidly with increasing angle of attack. It is recommended to control the angle of attack within the range of 0°–6°. The lift-to-drag ratio increases slowly with increasing flight speed, suggesting that the flight speed should be maintained near the maximum cruise speed. The lift-to-drag ratio generally decreases with increasing flight altitude, and it is recommended to maintain the flight altitude within the range of 9 km–11 km.

A Multi Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study’s findings present optimism for smart farm management and accurate SF by improving UAV systems’ response time and scalability.

Aerodynamic Analysis and Simulation for a Z-Shaped Folding Wing UAV

The potential of folding wing unmanned aerial vehicles (UAVs) in enhancing the adaptability of complex missions is substantial. Analyzing the aerodynamic characteristics of these UAVs is vital for optimizing the design of their folding wing configuration and airfoil shape. In this study, a model of a Z-shaped folding wing UAV was established with the objective of enhancing UAV maneuverability. An integrated vortex lattice method (VLM) was employed to analyze the aerodynamic characteristics of the Z-shaped folding wing and numerical simulations were utilized to validate the obtained results, which prove the effectiveness of the integrated VLM in studying aerodynamic characteristics at a high angle of attack of the Z-shaped folding wing UAV. This is a new way to reduce computation time while maintaining accuracy to calculate such complex folding structures in high fidelity. Furthermore, the Z-shaped folding wing configuration demonstrates enhanced maneuverability at high angles of attack, and a Simulink flight simulation model based on the aerodynamic coefficients of the Z-shaped folding wing verifies this property. This analysis can properly predict the post-stall characteristics for the Z-shaped folding wing UAV to ensure the safety of the vehicle during flight.

G-YOLO: A Lightweight Infrared Aerial Remote Sensing Target Detection Model for UAVs Based on YOLOv8

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes.

Parameter tuning for active disturbance rejection control of fixed-wing UAV based on improved bald eagle search algorithm

PurposeThis paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.Design/methodology/approachA six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.FindingsInspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.Practical implicationsThe proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.Originality/valueThe CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.

Measurement-Based Tapped Delay Line Channel Modeling for Fixed-Wing Unmanned Aerial Vehicle Air-to-Ground Communications at S-Band

Fixed-wing unmanned aerial vehicles (UAVs) are widely considered as a vital candidate of aerial base station in beyond Fifth Generation (B5G) systems. Accurate knowledge of air-to-ground (A2G) wireless propagation is important for A2G communication system development and testing where, however, there is still a lack of A2G wideband channel models for such a purpose. In this paper, we present a wideband fixed-wing UAV-based A2G channel measurement campaign at 2.7 GHz, and consider typical flight phases, based on which a wide-sense stationary uncorrelated scattering (WSSUS)-based tapped delay line (TDL) wideband channel model is proposed. Parameters of individual channel taps are analyzed in terms of gain, amplitude distribution, Rice factor and delay-Doppler spectrum. It is shown that UAV flight phases significantly influence the channel tap parameters. Particularly, the “Bell”-type spectrum is found to be the most suitable model for the delay-Doppler spectrum under various flight scenarios for A2G propagation. The proposed channel model can provide valuable assistance and guidance for UAV communication system evaluation and network planning.

A Novel Approach for Trajectory Tracking Control of an Under-Actuated Quad-Rotor UAV

A novel Udwadia-Kalaba approach for the trajectory tracking control of an under-actuated quad-rotor unmanned aerial vehicle (UAV) is presented. Compared to standard control approaches, the desired trajectories are treated as constraints called trajectory tracking constraints in this approach. Neither making any approximations or linearization of the nonlinear system nor imposing any a priori structure on the nature of the nonlinear controller, this methodology provides closedform nonlinear control. The control inputs satisfying the desired trajectory requirements can be obtained explicitly in compact closed form by solving Udwadia-Kalaba equation. Nonlinear dynamics modeling of a quad-rotor UAV is processed and a desired trajectory is given in this paper to illustrate this approach. The theoretical analysis and MATLAB simulation results verify the validity and efficiency of this approach. The real-time servo constraint forces are obtained conveniently and the quad-rotor UAV's movement meets the designed trajectory precisely.

Rotor unmanned aerial vehicle localization in the building sheltered area based on millimetre‐wave frequency‐modulated continuous wave radar

AbstractRotor unmanned aerial vehicles (UAVs) play an important role in both military and civilian fields nowadays. The safety risks associated with the UAVs increase the urgent need for detecting UAVs in urban environments as well. Moreover, UAVs are easily located in the non‐line‐of‐sight (NLOS) building sheltered area relative to the radar, making it very challenging to detect and localise. A novel algorithm for localising the rotor UAV in the common L‐shaped street building sheltered area is proposed. First, the authors establish a multipath signal model for a rotor UAV hovering over an L‐shaped street scene, leveraging the frequency‐modulated continuous wave signal. Then, the multipath information of the UAV is extracted by identifying the rotating periodicity of the blade embedded in the time‐frequency spectrum of the received signal. The back projection imaging is then conducted on the UAV‐related multipath. After extracting multipath ghosts in the image, the street area, where the UAV locates, can be determined, and the UAV is further localised using the path reflection characteristics of this area. Simulations and practical experiments based on millimetre waves indicate that the proposed method can enable high‐accuracy estimation of rotor UAV in the NLOS building sheltered area.

Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

ABSTRACT Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring.

A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning

The widespread application of unmanned aerial vehicle (UAV) swarms has posed unique challenges for anomaly detection. Multi-modal noise from multi-source heterogeneous sensors during UAV swarm communication affects data quality, and limited data sharing between different UAV organisations restricts training a unified anomaly detection model. To address these problems, this study proposes a UAV swarm anomaly detection model based on a multi-modal denoising autoencoder and federated learning (L-MDAE). First, L-MDAE simulates noise by adding perturbations to the original data during UAV swarm communication. Second, according to the characteristics of UAV data noise, this study designs a new MSE loss function (normalised mean square error, NMSE) based on the normalised correlation coefficient. Furthermore, heterogeneous neural networks with NMSE are constructed to enhance the multi-modal noise–removal capability of the model. Finally, this study considers the UAV control node as the client and the ground control station as the server. Using a federated learning mechanism, L-MDAE is trained on a client dataset, and its parameters are integrated and distributed on the server. In this way, each UAV can effectively detect abnormal data using L-MDAE. Experimental results on five datasets, including ALFA, TLM and ITS, demonstrate that L-MDAE outperforms baseline and related models. When using ALFA, L-MDAE achieved an accuracy of 0.9919 and a swarm anomaly detection accuracy of 0.9901, approximately 2% higher than that of the baseline model.

Research on a Distributed Cooperative Guidance Law for Obstacle Avoidance and Synchronized Arrival in UAV Swarms

In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom dynamic model and a guidance control model for unmanned aerial vehicles (UAVs) are established, and the guidance commands are decomposed into control signals for the pitch and yaw planes. Secondly, based on the theory of dynamic inversion control, a temporal consistency guidance law for a single UAV is constructed. On the other hand, an improved artificial potential field theory is used and integrated with a predictive correction network to generate guidance commands for threat avoidance. A threshold smoothing method is employed to integrate the two guidance systems, and a cluster consultation mechanism is introduced to design a two-layer temporal synchronization architecture, which negotiates to change the synchronization time of the swarm to achieve the convergence of consistency once again. Finally, in typical application scenarios, simulation verification demonstrates the effectiveness of the control method proposed in this paper. The proposed control method achieves the guidance of UAV formations to synchronize their arrival at the target location under complex threat conditions.