Predefined-time formation control of UAV swarm under spatiotemporal constraints.

With the rapid development of drone technology, its applications in areas such as aerial photography, logistics, and environmental monitoring are becoming increasingly widespread. However, the complexity of low altitude flight environments poses a serious challenge to the collision avoidance capabilities of drones. A study has proposed an autonomous control method based on fuzzy neural networks. This method combines the ability of fuzzy logic to handle uncertainty and fuzzy information, as well as the powerful learning and generalization capabilities of neural networks, aiming to achieve accurate prediction of drone flight status and intelligent generation of collision avoidance strategies. By constructing a fuzzy neural network model, taking the flight parameters, environmental information, and obstacle data of the drone as inputs, and training and optimizing the network, collision avoidance control instructions are output to achieve autonomous control of the drone. The experimental results show that the proposed fuzzy neural network autonomous control method exhibits good collision avoidance performance in low altitude flight scenarios. This method can effectively identify and avoid obstacles, ensuring the safe flight of the drone. At the same time, this method has high real-time performance and robustness, and can work stably in complex and changing environments. This study not only provides a new solution for the collision avoidance problem of unmanned aerial vehicles in low altitude flight scenarios, but also offers new ideas for the application of fuzzy neural networks in the field of autonomous control of unmanned aerial vehicles.

This paper investigates the minimum-time trajectory optimization problem for unmanned aerial vehicles (UAVs) with practical constraints on velocity, thrust acceleration, and thrust direction. We present how to obtain its exact convex relaxation based on the concept of supporting hyperplane. A convex relaxation of the original problem is obtained by equivalent convexification of the dynamics and relaxing a nonlinear equality constraint. To make the convex relaxation exact, our contribution lies in proposing a method of replacing the objective function by a parameterized one and achieving exactness of the convex relaxation by iteratively updating one parameter. This innovative method is inspired by finding an appropriate supporting hyperplane to support the feasible set at the solution of the original problem. Based on the proposed method, we can design an algorithm to very efficiently find the solution of the original problem, and convergence is theoretically ensured. The proposed method can be readily extended to missions with obstacle avoidance constraints, where such constraints are simply linearized. We can design a double-loop algorithm, which has very robust convergence, to find minimum-time collision-free trajectories. Numerical examples are provided to demonstrate the effectiveness and high efficiency of the algorithms.

This article investigates the formation problem of unmanned aerial vehicles (UAVs) in presence of communication anomalies including delay and data loss. A formation controller based on distributed model predictive control (DMPC) is proposed for the three-dimensional (3D) kinematic model of UAV. According to the different roles of UAVs, asynchronous and synchronous communication methods are used for information interaction in leader-follower (LF) formation. In consideration of communication anomalies, the assumed prediction states are applied to maintain UAVs in the predefined formation, and the updated rules for the assumed states are designed. The formation cost functions are developed for the leader and follower, respectively. The terminal feedback controllers of leader and followers are designed by 3D error model and linear matrix inequality (LMI) to derive the assumed terminal state converging to the desired value in case of communication anomalies. Utilizing the assumed prediction states of the leader and follower in the presence of communication anomalies, the collision avoidance constraint and error constraint are built to ensure the safety and formation accuracy. It is proved that the tracking and formation errors of UAVs can converge to zero in abnormal communication. Finally, simulations are conducted to verify the effectiveness of the proposed method.

Based on uncertainty theory, this paper studies the problem of unmanned aerial vehicle (UAV) combat mission assignment under an uncertain environment. First, considering both the target value, which is the combat mission benefit gained from attacking the target, and the unit fuel consumption of UAV as uncertain variables, an uncertain UAV combat mission assignment model is established. And according to decisions under the realization of uncertain variables, the first stage generates an initial mission allocation scheme corresponding to the realization of target value, while the second stage dynamically adjusts the scheme according to the realization of unit fuel consumption; a two-stage uncertain UAV combat mission assignment (TUCMA) model is obtained. Then, because of the difficulty of obtaining analytical solutions due to uncertainty and the complexity of solving the second stage, the TUCMA model is transformed into an expected value-effective deterministic model of the two-stage uncertain UAV combat mission assignment (ETUCMA). A modified particle swarm optimization (PSO) algorithm is designed to solve the ETUCMA model to get the expected value-effective solution of the TUCMA model. Finally, experimental simulations of multiple UAV combat task allocation scenarios demonstrate that the proposed modified PSO algorithm yields an optimal decision with maximum combat mission benefits under a maximum iteration limit, which are significantly greater benefits than those for the mission assignment achieved by the original PSO algorithm. The proposed modified PSO exhibits superior performance compared with the ant colony optimization algorithm, enabling the acquisition of an optimal allocation scheme with greater benefits. This verifies the effectiveness and superiority of the proposed model and algorithm in maximizing combat mission benefits.

Abstract In this paper, output‐feedback containment control problem of unmanned aerial vehicle (UAV) swarm with input saturation is studied under distributed denial of service (DoS) attacks. Any edge in the communication network may be attacked independently. The considered distributed DoS attack may occur at any time, and there is no limit to the number of DoS attacks, which is more in line with the actual attack scenario. The leader UAVs are noncooperative, which are embedded with unknown input. Firstly, to estimate the state signals, distributed observers are designed. Then, a general form of distributed DoS attack model is established. To deal with distributed DoS attacks, compensators are constructed for each follower UAV to estimate its neighbor signals when DoS attacks occur. The compensator calibration mechanism is introduced to calibrate the estimated values once the communication is restored. In this way, the flexible switching between the estimated values and the real values is realized to improve the ability to respond to DoS attacks. Then, an auxiliary system is designed to cope with the possible input saturation phenomenon. Finally, a novel variable‐gain distributed containment control law is proposed, based on a quadratic term of the adaptive parameter, which shortens the convergence time and reduces the steady‐state error. It is proved that the proposed scheme ensures that all the state errors, including containment error, observation error, adaptive parameter, and auxiliary variable, are uniformly ultimately bounded, and the effectiveness and superiority are verified by simulation experiments and comparative analyses.

This paper utilizes the distributed model predictive control (DMPC) method to investigate the formation control problem of unmanned aerial vehicles (UAVs) in the obstacle environment and establishes cooperative capability evaluation metrics of the swarm. Based on the DMPC approach, the formation cost function is constructed to adjust the relative positions and velocities of UAVs, ensuring the desired formation. Additionally, to address the obstacle avoidance problem in the formation, the obstacle avoidance function is designed to provide safe formation control in the obstacle environment. To evaluate the cooperative capability of UAVs, we design evaluation metrics from multiple dimensions to reflect the swarm’s cooperative capability. Finally, the simulation results show the effectiveness of the formation control method with obstacle avoidance and the applicability of the swarm’s cooperative capability evaluation metrics.

In order to address the problem of Unmanned Aerial Vehicles (UAVs) being difficult to locate in environments without Global Navigation Satellite System (GNSS) signals or with weak signals, this paper proposes a localization method for UAV aerial images based on semantic topological feature matching. Unlike traditional scene matching methods that rely on image-to-image matching technology, this approach uses semantic segmentation and the extraction of image topology feature vectors to represent images as patterns containing semantic visual references and the relative topological positions between these visual references. The feature vector satisfies scale and rotation invariance requirements, employs a similarity measurement based on Euclidean distance for matching and positioning between the target image and the benchmark map database, and validates the proposed method through simulation experiments. This method reduces the impact of changes in scale and direction on the image matching accuracy, improves the accuracy and robustness of matching, and significantly reduces the storage requirements for the benchmark map database.

ABSTRACTIn this paper, a Multi‐strategy Enhanced Wolf Pack Algorithm (MSEWPA) is proposed to address the three‐dimensional (3D) path planning problem for unmanned aerial vehicles (UAVs) in complex environments. Initially, a mathematical model for 3D path planning is constructed, comprehensively considering constraints such as UAV operational efficiency, path safety risks, performance limitations, obstacle avoidance requirements, and noise limits in urban functional areas. Subsequently, the design of the MSEWPA algorithm is elaborated in detail, including the utilization of the Good Lattice Point (GLP) theory to optimize population initialization for enhanced global search capability, the integration of selection, crossover, and mutation operations from the Differential Evolution (DE) algorithm to augment the randomness of wandering, the introduction of a behavior transition factor for adaptive behavior adjustment, the incorporation of light propagation phenomena to improve random search capabilities during the running process, and the design of multiple siege strategies to guide the exploration of globally optimal solutions. To validate the robustness of the algorithm, sensitivity analysis is conducted on key parameters to determine their optimal settings, and ablation experiments are performed to verify the effectiveness of each improvement strategy. Experimental results on the CEC‐2017 benchmark test functions demonstrate that MSEWPA excels in solving complex optimization problems, achieving rapid convergence to high‐quality global optimal solutions. Furthermore, in four path planning problems of varying complexity, MSEWPA outperforms 11 other state‐of‐the‐art metaheuristic optimization algorithms, demonstrating a strong balance between global and local exploration capabilities. This provides an effective solution for UAV 3D path planning.

In this article, we investigate the problem of unmanned aerial vehicles (UAVs) formation tracking in the presence of multiple cyber-threats. In this scenario, UAVs have private, potentially conflicting objectives, and their communication networks and local feedback mechanisms are vulnerable to sabotage and eavesdropping by malicious attackers. To address this real-world challenge, we propose a control scheme based on a twin-network structure. Specifically, we construct a virtual twin layer interconnected with the physical layer to design a resilient estimator that fortifies information exchange among UAVs under threats. In addition, by coupling the states from the twin layer and time-varying signals as masks, we achieve privacy protection for critical information. Furthermore, leveraging reliable data provided by the resilient estimator, we design a cooperative controller based on gradient-optimization to update the UAVs' positions. Using Lyapunov theory, we prove that the position of all UAVs converge to a dynamic Nash equilibrium. Finally, we conduct experimental studies to validate the effectiveness of the proposed control scheme.

Abstract The problem of how to effectively track and intercept small aircraft that break into the no-fly zones is now attracting increasing interest in robotics society. Vision-based control has been proved an effective solution to the target tracking problem for unmanned aerial vehicles (UAVs). Due to the limited field of view (FOV) of onboard vision sensors, existing works assume that the target is always detectable during tracking or limit the flight speed of the UAV in practice. In this paper, inspired by the broad FOV of camera network, we are the first to propose an eye-to-hand (i.e., fixed cameras) visual servoing scheme to track and intercept aerial targets by using UAVs and ground visual sensors. Specifically, utilizing rotation matrices, we first present a visual servoing equation to convert the UAV motion in image planes to the inertial frame. Then, an image-based visual servoing controller is designed directly based on image errors of camera nodes in the sensor network, and system stability is proved by means of Lyapunov analysis. Additionally, to achieve the desired translational velocity command, a low-level attitude controller is developed based on the UAV dynamics. Finally, a series of experiments in both simulated and real flight scenarios show the outstanding efficacy of our method.

This letter addresses the problem of unmanned aerial vehicles (UAVs)-based localization for a directional emitter with unknown position and transmission orientation. We propose an efficient three-step alternating estimator with iteratively reweighted least square (TA-IRLS). Based on linearized measurement models, the initial estimation of emitter position and orientation are obtained using low-complexity least squares (LS) approaches. To further enhance localization accuracy, IRLS is employed to refine measurement weights based on estimation residuals. Numerical results validate the superiority of the proposed TA-IRLS in terms of both localization efficiency and accuracy.

This paper investigates the attitude control problem of unmanned aerial vehicles (UAVs), especially in the presence of uncertainties and external disturbances. To address this challenge, a fractional-order reaching law sliding mode with active disturbance rejection controller (FOSM-ADRC) is proposed. The controller combines a fractional-order calculus operator and active disturbance rejection controller (ADRC) techniques to enhance the dynamic performance and robustness of the system. Through the inner and outer loop design, the jitter of the sliding mode controller (SMC) is effectively suppressed, and fast response and strong anti-jamming ability are achieved, which, in turn, improves the control accuracy. Firstly, the dynamic model of the UAV is established, and its nonlinear dynamic characteristics are analyzed in detail. On this basis, a fractional-order reaching law sliding mode controller (FO-SMC) is designed as the outer loop to achieve fast response. ADRC is employed in the inner loop to compensate for the internal and external disturbances of the system. The results show that the FOSM-ADRC can effectively suppress the jitter phenomenon and maintain good control performance.

PurposeA multivariable model reference adaptive control method is proposed to solve a distributed leader–follower formation control problem of unmanned aerial vehicles (UAVs) with uncertain parameters and unknown external disturbances for both leader and followers.Design/methodology/approachA case of uncertain stochastic external disturbances for UAVs is considered, and based on the distributed communication network of UAVs, a state-feedback adaptive controller is proposed to maintain the formation of UAVs consistently. Then, the stability and asymptotic tracking performance of the UAV formation control system are analyzed by the Lyapunov function.FindingsThe simulation results demonstrate that this formation control scheme can effectively solve the stochastic external disturbance problem of UAVs and ensure the stability of their formation.Originality/valueThe proposed multivariable model reference adaptive control method reduces the error of formation control system and improves the stability and control performance of UAV compared with fixed control.

In this study, we focus on the path-planning problem of unmanned aerial vehicles (UAVs) deployed for inspection missions at target points. The goal is to visit each target point, provide revisits to important target points, and ultimately meet the monitoring requirements with regular and stable monitoring frequencies. Herein, we present MTSP-R, a novel variant of the multiple traveling salesmen problem (MTSP), in which revisits to important target points are allowed. We address the path-planning problem of multi-UAV in two stages. First, we propose a nearest insertion algorithm with revisits (NIA-R) to determine the number of required UAVs and initial inspection paths. We then propose an improved genetic algorithm (IGA) with two-part chromosome encoding to further optimize the inspection paths of the UAVs. The simulation results demonstrate that the IGA can effectively overcome the shortcomings of the original genetic algorithm, providing shorter paths for multiple UAVs and more stable monitoring frequencies for the target points.

Collision-free trajectory planning for UAVs based on sequential convex programming

This study primarily studies the shortest-path planning problem for unmanned aerial vehicle (UAV) formations under uncertain target sequences. In order to enhance the efficiency of collaborative search in drone clusters, a compensation look-ahead algorithm based on optimizing the four-point heading angles is proposed. Building upon the receding-horizon algorithm, this method introduces the heading angles of adjacent points to approximately compensate and decouple the triangular equations of the optimal trajectory, and a general formula for calculating the heading angles is proposed. The simulation data indicate that the model using the compensatory look forward algorithm exhibits a maximum improvement of 12.9% compared to other algorithms. Furthermore, to solve the computational complexity and sample size requirements for optimal solutions in the Dubins multiple traveling salesman model, a path-planning model for multiple UAV formations is introduced based on the Euclidean traveling salesman problem (ETSP) pre-allocation. By pre-allocating sub-goals, the model reduces the computational scale of individual samples while maintaining a constant sample size. The simulation results show an 8.4% and 17.5% improvement in sparse regions for the proposed Euclidean Dubins traveling salesman problem (EDTSP) model for takeoff from different points.

Distributed time-varying Nash equilibrium in resilient multi-objective formation control for cyber–physical systems

A constrained control framework for unmanned aerial vehicles based on Explicit Reference Governor

Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.