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The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

The purpose of this research is to propose a novel method to plan paths of unmanned aerial vehicle (UAV) formations. This is to make use of the aerodynamic advantage of formation flight to reduce energy consumption of UAVs.

The method proposed in this research make use of the fact that, under certain conditions, the regions where if a UAV rendezvous or separates with another UAV would save energy by formation flying can be analytically calculated. The intersections of these regions are used to decide which UAV are to fly in the same formation. This combination of which UAV are to fly together and what order they join/part from the formation is called the topology of the problem.

The proposed method was proved to be effective in identifying efficient topologies, with the majority of the topologies selected falling below 5 percent error rate in terms of energy.

The originality of this research lies in the fact that the proposed method is completely visualised – all the necessary information to organise formations is visualised in the envelopes. Still, the proposed method was proved to be effective in selecting efficient topologies.

The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown parameters; a more general nonlinear dynamical model for each UAV is considered to include two terms. Due to an unknown parameter corresponding to the normal or abnormal state for each UAV, the bias-compensated approach is proposed to obtain the unbiased parameter estimation. Meanwhile, the biased error and accuracy analysis are also given in case of strict statistical description of the uncertainty or white noise. To relax this strict statistical description on external noise, an analytic center approach is proposed to identify the unknown parameters in presence of bounded noise, such that two inner and outer ellipsoidal approximations are constructed to include the membership set. To be precise, this paper is regarded as one extension and summary for the author’s previous research on the anomaly detection in multi-UAV formation. Finally, one simulation example is given to confirm the theoretical results.

Firstly, one extended nonlinear relation is constructed to embody the mutual relationship of UAVs. Secondly, to obtain the unbiased parameter estimations, the bias-compensated approach is applied to achieve it under the condition of white noise. Thirdly, in case of unknown but bounded noise, an analytic center approach is proposed to deal with this special case. Without loss of generality, the author thinks this paper can be used as one extension and summary for research on multi-UAVs formation anomaly detection.

An anomaly detection problem in multi-UAVs formation can be transformed into a problem of nonlinear system identification, and in modeling the nonlinear dynamical model for each UAV, two terms are considered simultaneously to embody the mutual relationships with other nearest UAV.

To the best knowledge of the authors, this problem of the anomaly detection problem in multi-UAVs formation is proposed by the authors’ previous work, and the problem of multi-UAVs formation anomaly detection can be transferred into one problem of parameter identification. In case of unknown but bounded noise, an analytic center approach is proposed to identify the unknown parameters, which correspond to achieve the goal of the anomaly detection.

The purpose of this paper is to design an integrated guidance and control design for a formation flight of four unmanned aerial vehicles to follow a moving ground target.

The guidance law is based on the line‐of‐sight. The control is optimal. The guidance law is integrated with the optimal control law and is applied to a linear dynamic model.

The theoretical results are supported by the numerical simulations that illustrate a coordinated encirclement of a ground maneuvering target.

A linear dynamic UAV model and a liner engine model were employed.

This is expected to provide efficient coordination technique required in many civilian circular formation UAV applications; also the technique can be used to provide a safe environment required for the civil applications.

The research will facilitate the deployment of autonomous unmanned aircraft systems in various civilian applications such as border monitoring.

The research addresses the challenges of coordination of multiple unmanned aerial vehicles in a circular formation using an integrated optimal control technique with line‐of‐sight guidance.

The purpose of this paper is to investigate time-optimal control problems for multiple unmanned aerial vehicle (UAV) systems to achieve predefined flying shape.

Two time-optimal protocols are proposed for the situations with or without human control input, respectively. Then, Pontryagin’s minimum principle approach is applied to deal with the time-optimal control problems for UAV systems, where the cost function, the initial and terminal conditions are given in advance. Moreover, necessary conditions are derived to ensure that the given performance index is optimal.

The effectiveness of the obtained time-optimal control protocols is verified by two contrastive numerical simulation examples. Consequently, the proposed protocols can successfully achieve the prescribed flying shape.

This paper proposes a solution to solve the time-optimal control problems for multiple UAV systems to achieve predefined flying shape.

This paper aims to propose a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more accurately. Thus, the controls should ensure that the UAV swarm follows a moving circular path whose center is the target while also attaining and maintaining a circular formation of a specific geometric shape; and the formation control system is capable of self-tuning because the UAV dynamics is uncertain.

This paper considers two interaction architectures: an open-chain where each UAV only interacts with its neighbors; and a cooperative leader, where the leading UAV is involved in attaining the formation. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC).

Using open-chain decentralized architecture allows to have an unlimited number of aircraft in a formation, which is in line with the swarm behavior concept. The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models.

Assume the target follows a rectilinear trajectory at a constant speed. The speed is supposed to be known in advance. Another assumption is that the weather is windless.

In contrast to known studies, this one uses Lyapunov guidance vector fields that are direction- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus.

The purpose of this paper is to find a solution for the unmanned aerial vehicle (UAV) rendezvous problem, which should be feasible, optimal and not time consuming. In the existing literatures, the UAV rendezvous problem is always presented as a matter of simultaneous arrival. They focus only on the time consistency. However, the arrival time of UAVs can vary according to the rendezvous position. The authors should determine the best rendezvous position with considering UAVs’ maneuver constraint, so that UAVs can construct a formation in a short time.

The authors present a decentralized method in which UAVs negotiate with each other for the best rendezvous positions by using Nash bargain. The authors analyzed the constraints of the rendezvous time and the UAV maneuver, and proposed an objective function that allows UAVs to get to their rendezvous positions as fast as possible. Bezier curve is adopted to generate smooth and feasible flight trajectories. During the rendezvous process, UAVs adjust their speed so that they can arrive at the rendezvous positions simultaneously.

The effectiveness of the proposed method is verified by simulation experiments. The proposed method can successfully and efficiently solve the UAV rendezvous problem.

As far as the authors know, it is the first time Nash bargain is used in the UAV rendezvous problem. The authors modified the Nash bargain method and make it distributed, so that it can be computed easily. The proposed method is much less consuming than ordinary Nash bargain method and ordinary swarm intelligence based methods. It also considers the UAV maneuver constraint, and can be applied online for its fast calculation speed. Simulations demonstrate the effectiveness of the proposed method.

The successful application of the group of unmanned aerial vehicles (UAVs) in the tasks of monitoring large areas is becoming a promising direction in modern robotics. This paper aims to study the tasks related to the control of the UAV group while performing a common mission.

This paper discusses the main tasks solved in the process of developing an autonomous UAV group. During the survey, five key tasks of group robotics were investigated, namely, UAV group control, path planning, reconfiguration, task assignment and conflict resolution. Effective methods for solving each problem are presented, and an analysis and comparison of these methods are carried out. Several specifics of various types of UAVs are also described.

The analysis of a number of modern and effective methods showed that decentralized methods have clear advantages over centralized ones, since decentralized methods effectively perform the assigned mission regardless of on the amount of resources used. As for the method of planning the group movement of UAVs, it is worth choosing methods that combine the algorithms of global and local planning. This combination eliminates the possibility of collisions not only with static and dynamic obstacles, but also with other agents of the group.

The results of scientific research progress in the tasks of UAV group control have been summed up.

This paper aims to investigate the distributed formation control problem for a multi-quadrotor unmanned aerial vehicle system without linear velocity feedbacks.

A nonlinear controller is proposed based on the orthogonal group SE(3) to obviate singularities and ambiguities of the traditional parameterized attitude representations. A cascade structure is applied in the distributed controller design. The inner loop is responsible for attitude control, and the outer loop is responsible for translational dynamics. To ensure a linear-velocity-free characteristic, some auxiliary variables are introduced to construct virtual signals in distributed controller design. The stability analysis of the proposed distributed control method by the Lyapunov function is provided as well.

A group of four quadrotors with constant reference linear velocity and a group of six quadrotors with varying reference linear velocity are adopted to verify the effectiveness of the proposed strategy.

This is a new innovation for multi-robot formation control method to improve assembly automation.

The purpose of this study is to address the limitations of existing target group distribution pattern analysis methods and identify subtle distribution differences within and between the groups with no pre-specified distribution features. Classical work generally concentrates on either the group distribution tendency or shape as a whole and simply ignores the subtle distribution differences within the group. Other work is constrained to pre-defined spatial distribution features.

This study proposes a novel algorithm for target group distribution pattern analysis. This study first transforms the group distribution data with uncertain measurements into a distributional image. Upon that, a bagged convolutional neural network model is constructed to discriminate the delicate group distribution patterns.

Experimental results indicate that our method is robust to target missing and location variance and scalable with dataset size. Our method has outperformed the benchmark machine learning methods significantly in pattern identification accuracy.

Our method is applicable for complex unmanned aerial vehicle distribution pattern identification.