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Multi-UAV (unmanned aerial vehicle) target enclosing control is one of the key technologies for achieving cooperative tasks. It faces limitations in communication resources and task framework separation. To address this, a distributed cooperative control strategy is proposed based on dynamic time-varying formation description and event-triggering mechanism. Firstly, a formation description method based on a geometric transformation parameter set is established to uniformly describe the translation, rotation, and scaling movements of the formation, providing a foundation for time-varying formation control. Secondly, a cooperative architecture for adaptive target enclosing tasks is designed. This architecture achieves an organic combination of formation control and target enclosing in a unified framework, thereby meeting flexible transitions between multiple formation patterns such as equidistant surrounding and variable-distance enclosing. Thirdly, a distributed dynamic event-triggered cooperative enclosing controller is developed. This strategy achieves online adjustment of communication thresholds through internal dynamic variables, significantly reducing communication while strictly ensuring system performance. By constructing a Lyapunov function, the stability and Zeno free behavior of the closed-loop system are proven. The simulation results verify this strategy, showing that this strategy can significantly reduce communication frequency while ensuring enclosing accuracy and formation consistency and effectively adapt to uniform and maneuvering target scenarios.

The recent development of unmanned aerial vehicle (UAV) technology has shown the possibility of using UAVs in many research and industrial fields. One of them is for UAVs moving in swarms to provide wireless networks in environments where there is no network infrastructure. Although this method has the advantage of being able to provide a network quickly and at a low cost, it may cause scalability problems in multi-hop connectivity and UAV control when trying to cover a large area. Therefore, as more UAVs are used to form drone networks, the problem of efficiently controlling the network topology must be solved. To solve this problem, we propose a topology control system for drone networks, which analyzes relative positions among UAVs within a swarm, then optimizes connectivity among them in perspective of both interference and energy consumption, and finally reshapes a logical structure of drone networks by choosing neighbors per UAV and mapping data flows over them. The most important function in the scheme is the connectivity optimization because it should be adaptively conducted according to the dynamically changing complex network conditions, which includes network characteristics such as user density and UAV characteristics such as power consumption. Since neither a simple mathematical framework nor a network simulation tool for optimization can be a solution, we need to resort to reinforcement learning, specifically DDPG, with which each UAV can adjust its connectivity to other drones. In addition, the proposed system minimizes the learning time by flexibly changing the number of steps used for parameter learning according to the deployment of new UAVs. The performance of the proposed system was verified through simulation experiments and theoretical analysis on various topologies consisting of multiple UAVs.

Single UAVs have limited capabilities for complex missions, so suitable solutions are needed to improve the mission success rate, as well as the UAVs’ survivability. A cooperative multi-UAV formation offers great advantages in this regard; however, for large and complex systems, the traditional control methods will be invalid when faced with unstable and changing environments. To deal with the poor self-adaptability and high requirements for the environmental state information of traditional control methods for a multi-UAV cluster, this paper proposes a consistent round-up strategy based on PPO path optimization to track targets. In this strategy, the leader is trained using PPO for obstacle avoidance and target tracking, while the followers are expected to establish a communication network with the leader to obtain environmental information. In this way, the tracking control law can be designed, based on the consistency protocol and the Apollonian circle, to realize the round-up of the target and obstacle avoidance. The experimental results show that the proposed strategy can achieve the round-up of the target UAV and guide the pursuing multi-UAV group to avoid obstacles in the absence of the initial detection of the target. In multiple simulated scenarios, the success rates of the pursuit multi-UAV cluster for rounding up the target are maintained above 80%.

This paper studies the formation control for unmanned aerial vehicles (UAVs) under communication delay and wake disturbances, in which the inner-loop and outer-loop control strategy is adopted. Firstly, as for the dynamical models of follower UAVs, the wake interferences are considered and their influences are respectively estimated by using the sliding model disturbance observers (SMDOs). Secondly, since the outer-loop information in the UAVs exchanges via communication network, by adding an internal dynamic variable, an adaptive memory-based event-triggered mechanism (METM) is proposed to alleviate transmission burden with maintaining ideal control performance. Thirdly, by using the designed METM and an intermediate vector, a sliding mode controller is derived to accomplish the desired control target, which can compensate the communication delay in control input. Fourthly, as for the overall closed-loop system, a sufficient condition on asymptotical stability is established and a co-design method of checking the triggering parameters and controller gains is expressed in term of linear matrix inequalities (LMIs). Moreover, in order to tackle the wake interferences of the inner-loop, an adaptive attitude tracking controller is put forward to ensure the bounded stability of tracking errors by solving the reference signal. Finally, a simulated example is exploited to illustrate the validity of the proposed scheme.

Existing research has made significant strides in UAV formation control, particularly in active localization and certain passive methods. However, these approaches face substantial limitations in electromagnetically silent environments, often relying on strong assumptions such as fully known and stationary emitter positions. To overcome these challenges, this paper proposes a comprehensive framework for bearing-only passive localization and adjustment of UAV formations under strict electromagnetic silence constraints. We systematically develop three core models: (1) a geometric triangulation model for scenarios with three known emitters, enabling unique target positioning; (2) a hierarchical identification mechanism leveraging an angle database to resolve label ambiguity when some emitters are unknown; and (3) a cyclic cooperative strategy, Perceive-Explore-Judge-Execute (PEJE), optimized via an improved genetic algorithm with adaptive discrete neighborhood search (GA-IADNS), for dynamic formation adjustment. Extensive simulations demonstrate that our proposed methods exhibit strong robustness, rapid convergence, and high adjustment accuracy across varying initial deviations. Specifically, after adjustment, the maximum radial deviation of all UAVs from the desired position is less than 0.0001 m, and the maximum angular deviation is within 0.00013°; even for the 30%R initial deviation scenario, the final positional error remains negligible. Furthermore, comparative experiments with a standard Genetic Algorithm (GA) confirm that GA-IADNS achieves superior performance: it reaches stable peak average fitness at the 6th generation (vs. no obvious convergence of GA even after 20 generations), reduces the convergence time by over 70%, and improves the final adjustment accuracy by more than 95% relative to GA. These results significantly enhance the autonomous collaborative control capability of UAV formations in challenging electromagnetic conditions.

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.

This paper addresses the analysis and the deployment of the network infrastructure based on multiple Unmanned Air Vehicles (UAVs). Despite the unprecedented potential to the mobility of the network infrastructure, there has been no effort to establish a mathematical model of the infrastructure and formation control strategies. We model the generic dynamics of the network infrastructure and derive the network throughput of the infrastructure. Through the parametrization of the model, we extract the generic factors of the network protocols and verify our model through the Network Simulator 3 (ns-3). By exploiting our network analysis model, we propose a novel formation control algorithm that determines the location of the UAVs to maximize the efficiency of the network. To achieve the objectives of the infrastructure, we define the formation-shaping effect as forces and elaborately design them using the generic factors. The formation algorithm continuously approaches to the optimized formation of a fleet of UAVs to enhance the overall throughput of the terrestrial devices. Our evaluations show that the algorithm guarantees remarkably higher throughput than the static formations. Through the dynamic transformation of the UAV formation, we believe that the multi-UAV-based network infrastructure could expand the boundary of the existing infrastructure while reducing the network traffic.

What are the main findings? * We propose a CTDE MARL framework that couples three lightweight attention branches (self, inter-agent, and entity) with a Pareto archive to learn interpretable vector-reward policies without fragile weight tuning. * In urban-like 3D simulations under partial observability, the framework improves team success by 13–27 percentage points for N = 2–5, while reducing collisions, maintaining tighter formations, and lowering control effort. We propose a CTDE MARL framework that couples three lightweight attention branches (self, inter-agent, and entity) with a Pareto archive to learn interpretable vector-reward policies without fragile weight tuning. In urban-like 3D simulations under partial observability, the framework improves team success by 13–27 percentage points for N = 2–5, while reducing collisions, maintaining tighter formations, and lowering control effort. What is the implication of the main finding? * The method acts as a plug-in for common MARL backbones (instantiated on MADDPG here) and scales to larger teams with stable training and smoother trade-offs. * It offers a practical path to jointly optimize safety and efficiency for real multi-UAV deployments without repeated reward re-weighting. The method acts as a plug-in for common MARL backbones (instantiated on MADDPG here) and scales to larger teams with stable training and smoother trade-offs. It offers a practical path to jointly optimize safety and efficiency for real multi-UAV deployments without repeated reward re-weighting. Autonomous multi-UAV formation control in cluttered urban environments remains challenging due to partial observability, dense and dynamic obstacles, and conflicting objectives (task efficiency, energy use, and safety). Yet many MARL-based approaches still collapse vector-valued objectives into a single hand-tuned reward and lack selective information fusion, leading to brittle trade-offs and poor scalability in urban clutter. We introduce a model-agnostic MARL framework—instantiated on MADDPG for concreteness—that augments a CTDE backbone with three lightweight attention modules (self, inter-agent, and entity) for selective information fusion, and a Pareto optimization module that maintains a compact archive of non-dominated policies to adaptively guide objective trade-offs using simple, interpretable rewards rather than fragile weightings. On city-scale navigation tasks, the approach improves final team success by 13–27 percentage points for N = 2–5 while simultaneously reducing collisions, tightening formation, and lowering control effort. These gains require no algorithm-specific tuning and hold consistently across the tested team sizes (N = 2–5), underscoring a stronger safety–efficiency trade-off and robust applicability in cluttered, partially observable settings.

A flight of a UAV formation is an efficient way to implement surveillance and reconnaissance operations. The usage of a few UAVs as a formation instead of a single vehicle allows creating a distributed network of sensors, which decreases the duration of flight missions and enlarges a total field of view. From a practical point of view, implementations of formation flights require taking into account several separate aspects of flight of UAV such as a quick take-off of several aircraft, aggregating all UAVs in the same space to create swarm and collective flight of the formation towards the area of a surveillance mission. The paper presents the results of researches and experiments carried out towards practical solutions to those aspects. A magnetic launcher is an excellent appliance to put UAV in the air, and its operation could be repeated quickly. Hence, it is ideal to be used in a formation flight. The leader-follower approach based on two-stage switching control is an effective method to aggregate UAVs in the same space while they are flying over large areas. Whereas, the decentralized control of aerial flocking can be used to achieve a coherent flight of UAV formation, which is able to self-organize. Results from simulations and experiments show the effectiveness of each presented aspect and prove their usability in the implementation of formation flights.

This paper provides an in-depth analysis of the current research landscape in the field of UAV (Unmanned Aerial Vehicle) swarm formation control. This review examines both conventional control methods, including leader–follower, virtual structure, behavior-based, consensus-based, and artificial potential field, and advanced AI-based (Artificial Intelligence) methods, such as artificial neural networks and deep reinforcement learning. It highlights the distinct advantages and limitations of each approach, showcasing how conventional methods offer reliability and simplicity, while AI-based strategies provide adaptability and sophisticated optimization capabilities. This review underscores the critical need for innovative solutions and interdisciplinary approaches combining conventional and AI methods to overcome existing challenges and fully exploit the potential of UAV swarms in various applications.