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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.

The chapter relates to advanced management of large distributed dynamic systems in unpredictable and crisis situations. It briefs the DARPA Mosaic Warfare concept and shows its possible expression under SGT together with exemplary solutions for such tasks as grouping of scattered elements into more powerful forces with unified control, and observation and elimination of dangerous elements by collective operation of causal forces around them. Of practical importance may be mosaics-related approaches using massive robotics. It is shown in SGL how easy to assemble teams of UCAVs for intelligent swarming, self-restructuring and observing territory with collection, distribution and impacting of targets discovered. Another SGL scenario organizes automatic fight of aerial swarm with other group/swarm, autonomously and without external control. It is also shown how broken into pieces platoon of unmanned vehicles, due to situations on roads, is self-recomposing into normal platoon chain again, with vehicles symbolically considered as mosaic tiles.

The purpose of this paper is to propose a new algorithm for in‐mission trajectories and speed adjustment of multiple unmanned aerial vehicles (UAVs) participating in a mission that requires them to arrive at target location simultaneously with switching and imperfect communication among the vehicles.

This algorithm, programmed at each UAV level, is based on the repeated consensus seeking among the participating vehicles about the time‐on‐target (ToT) through an imperfect inter‐vehicle communication link. The vehicles exchange their individual ToT values repeatedly for a particular duration to pick the highest value among all the vehicles in communication. A consensus confidence flag is set high when consensus is successful. After every consensus cycle with high confidence value, the mission adjustment is carried out by computing difference value between ToT consensus and a threshold value. For the difference values higher than a certain limit, vehicle's trajectory is adjusted by in‐mission insertion of new waypoint (WP) and for lower values the vehicle's speed is varied under allowable limits. The consensus seeking followed by the mission adjustment is repeated periodically to quash the imperfect communication effects.

A mathematical analysis has been carried out to establish the conditions for convergence of the algorithm. The simultaneous arrival of the vehicles subjected to switching communication is achieved only when the union of the switching links during the consensus period enables a vehicle to receive information from all the other vehicles and the switching rate is sufficiently high. This algorithm has been tested in a 6‐degree‐of‐freedom (DoF) multiple UAV simulation environment and achieves simultaneous arrival of multiple fixed wing UAVs under imperfect communication links that meets the aforementioned conditions.

The presented algorithm and design strategy can be extended for other types of cooperative control missions where certain variable of interest is shared among all the vehicles over imperfect communication environment. The design is modular in functionality and can be incorporated into existing vehicles or simulations.

This research presents a new consensus algorithm that repeatedly performs polling of ToT among the vehicles through intermittent communication. The continual nature of consensus seeking covers the weakness of the imperfect communication. A two‐level mission adjustment provides better accuracy in simultaneous arrival at the target location.

If these tasks are broken down by aircrew position, the pilot (often called the operator) is the prime command and control coordinator, while the second crew member (the sensor operator) is responsible for collecting, processing and communicating sensor data. There is often an overlap of duties between these two crew members, but the operation of smaller UAVs is commonly only controlled by these two personnel. An illustration of the ground control shelter interface used by a typical Army UAV pilot (AVO, Air Vehicle Operator) for the US Army Shadow UAV follows (Fig. 1).

Unmanned aerial vehicles (UAVs) have a wide variety of applications such as surveillance and search. Many of these tasks are better executed by multiple UAVs acting as a group. One of the main problems to be tackled in a high‐density UAV traffic scenario is that of collision avoidance among UAVs. The purpose of this paper is to give a collision avoidance algorithm to detect and resolve the conflicts of projected path among UAVs.

The collision avoidance algorithm developed in the paper handles multiple UAV conflicts by considering only the most imminent predicted collision and doing a maneuver to increase the line‐of‐sight rate to avoid that conflict. After the collision avoidance maneuver, the UAVs fly to their destinations via Dubins shortest path to minimize time to reach destination. The algorithm is tested on realistic six degree of freedom UAV models augmented with proportional‐integral controllers to hold altitude, velocity, and commanded bank angles.

The paper shows, through extensive simulations, that the proposed collision avoidance algorithm gives a good performance in high‐density UAV traffic scenarios. The proposed collision avoidance algorithm is simple to implement and is computationally efficient.

The algorithm developed in this paper can be easily implemented on actual UAVs.

There are only a few works in the literature that address multiple UAV collision avoidance in very high‐density traffic situations. This paper addresses very high‐density multiple UAV conflict resolution with realistic UAV models.

Cooperative control of a group of unmanned aerial vehicles (UAVs) is an important area of research. The purpose of this paper is to explore multi-UAV control in the framework of providing surveillance of areas of interest with automatic loss detection and replacement capabilities.

The research is based on the concept of the multi-agent system. The authors present the framework of the multi-agent and protocol design for monitoring the network of a group of UAVs.

If one or more UAVs which is conducting a high priority surveillance task is lost, the system can self-arrange for another UAV to replace the lost UAV and continue to execute its task. This research provides an excellent design protocol for UAV loss detection and replacement scheme.

One of the major limitations of this research is that we have only two types of priority levels, high or low. If the priority is more than two levels, for example, high priority 1, high priority 2, or high priority 3, the replacement has not yet been implemented.

This paper contributes to the following two aspects of the scientific knowledge. The first contribution is the design of an agent model which jointly considers system architecture, communication, control logic and target monitoring. The second contribution includes the decentralized and automatic UAV loss detection and replacement algorithm.

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 time delay would occurs when the measurements of multiple unmanned aerial vehicles (UAVs) are transmitted to the date processing center during cooperative target localization. This problem is often named as the out-of-sequence measurement (OOSM) problem. This paper aims to present a nonlinear filtering based on solving the Fokker–Planck equation to address the issue of OOSM.

According to the arrival time of measurement, the proposed nonlinear filtering can be divided into two parts. The non-delay measurement would be fused in the first part, in which the Fokker–Planck equation is utilized to propagate the conditional probability density function in the forward form. The time delay measurement is fused in the second part, in which the Fokker–Planck is used in the backward form approximately. The Bayes formula is applied in both parts during the measurement update.

Under the Bayesian filtering framework, this nonlinear filtering is not only suitable for the Gaussian noise assumption but also for the non-Gaussian noise assumption. The nonlinear filtering is applied to the cooperative target localization problem. Simulation results show that the proposed filtering algorithm is superior to the previous Y algorithm.

In this paper, the research shows that a better performance can be obtained by fusing multiple UAV measurements and treating time delay in measurement with the proposed algorithm.

In this paper, the OOSM problem is settled based on solving the Fokker–Planck equation. Generally, the Fokker–Planck equation can be used to predict the probability density forward in time. However, to associate the current state with the state related to OOSM, it would be used to propagate the probability density backward either.

The purpose of this paper is to investigate the time-varying finite-time formation tracking control problem for multiple unmanned aerial vehicle systems under switching topologies, where the states of the unmanned aerial vehicles need to form desired time-varying formations while tracking the trajectory of the virtual leader in finite time under jointly connected topologies.

A consensus-based formation control protocol is constructed to achieve the desired formation. In this paper, the time-varying formation is specified by a piecewise continuously differentiable vector, while the finite-time convergence is guaranteed by utilizing a non-linear function. Based on the graph theory, the finite-time stability of the close-loop system with the proposed control protocol under jointly connected topologies is proven by applying LaSalle’s invariance principle and the theory of homogeneity with dilation.

The effectiveness of the proposed protocol is verified by numerical simulations. Consequently, the proposed protocol can successfully achieve the predefined time-varying formation in finite time under jointly connected topologies while tracking the trajectory generated by the leader.

This paper proposes a solution to simultaneously solve the control problems of time-varying formation tracking, finite-time convergence, and switching topologies.