Follower Unmanned Aerial Vehicles Research Articles

Distributed formation control for leader‐follower quadrotor unmanned aerial vehicles system based on practical sliding mode and event‐triggered mechanisms

AbstractThis article investigates the distributed formation trajectory tracking problem of a homogeneous quadrotor unmanned aerial vehicles (UAVs) system using a leader‐follower consensus protocol in three‐dimensional space. To enable the follower UAVs in the swarm to track a single leader UAV and form a desired formation under undirected topology, a six‐degree‐of‐freedom dynamic model with bounded time‐varying disturbances is established, which is divided into a tracking subsystem and a stabilizing subsystem. A practical sliding mode control (PSMC) based event‐triggered algorithm is proposed to achieve distributed formation control for a leader‐follower quadrotor UAVs system and reduce the network transmission resources. To address the potential issue of inter‐UAVs collisions during formation, an artificial potential field (APF) method is introduced in the tracking subsystem, and an disturbance observer is introduced in the stabilizing subsystem to estimate the unknown external disturbances. The boundedness of signals in the closed‐loop system is demonstrated using Lyapunov stability theory, and the exclusion of Zeno behavior can be ensured by having a positive lower bound on the inter‐event time between two consecutive triggering events. Finally, the effectiveness of the proposed event‐triggered algorithm is validated through simulation examples.

Reinforcement Learning-Based Fractional-Order Adaptive Fault-Tolerant Formation Control of Networked Fixed-Wing UAVs With Prescribed Performance.

This article investigates the fault-tolerant formation control (FTFC) problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults. To constrain the distributed tracking errors of follower UAVs with respect to neighboring UAVs in the presence of faults, finite-time prescribed performance functions (PPFs) are developed to transform the distributed tracking errors into a new set of errors by incorporating user-specified transient and steady-state requirements. Then, the critic neural networks (NNs) are developed to learn the long-term performance indices, which are used to evaluate the distributed tracking performance. Based on the generated critic NNs, actor NNs are designed to learn the unknown nonlinear terms. Moreover, to compensate for the reinforcement learning errors of actor-critic NNs, nonlinear disturbance observers (DOs) with skillfully constructed auxiliary learning errors are developed to facilitate the FTFC design. Furthermore, by using the Lyapunov stability analysis, it is shown that all follower UAVs can track the leader UAV with predesigned offsets, and the distributed tracking errors are finite-time convergent. Finally, comparative simulation results are presented to show the effectiveness of the proposed control scheme.

Leader–follower UAVs formation control based on a deep Q-network collaborative framework

This study examines a collaborative framework that utilizes an intelligent deep Q-network to regulate the formation of leader–follower Unmanned Aerial Vehicles (UAVs). The aim is to tackle the challenges posed by the highly dynamic and uncertain flight environment of UAVs. In the context of UAVs, we have developed a dynamic model that captures the collective state of the system. This model encompasses variables like as the relative positions, heading angle, rolling angle, and velocity of different nodes in the formation. In the subsequent section, we elucidate the operational procedure of UAVs in a collaborative manner, employing the conceptual framework of Markov Decision Process (MDP). Furthermore, we employ the Reinforcement Learning (RL) to facilitate this process. In light of this premise, a fundamental framework is presented for addressing the control problem of UAVs utilizing the DQN scheme. This framework encompasses a technique for action selection known as ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon$$\\end{document}-imitation, as well as algorithmic specifics. Finally, the efficacy and portability of the DQN-based approach are substantiated by numerical simulation validation. The average reward curve demonstrates a satisfactory level of convergence, and kinematic link between the nodes inside the formation satisfies the essential requirements for the creation of a controller.

Angle-Constrained Formation Maneuvering of Unmanned Aerial Vehicles

In a global positioning system (GPS)-denied environment, unmanned aerial vehicles (UAVs) rely on local sensing-based formation maneuvering approaches for collective motion. To improve mission efficiency by reducing the total sensing requirements on all UAVs, this article proposes a leader–follower formation maneuvering framework with two leaders, where the followers will track the two leaders and maintain a desired angle-constrained formation with respect to the leaders using direction-only measurements, i.e., removing the need for inter-UAV distance measurements for the followers. To enable the UAV formation to maneuver in translation, rotation, and scaling simultaneously, the desired formation shape is specified by a set of interior angle constraints. By assigning each UAV’s yaw angle and position as the four controllable variables, an estimation-based attitude control algorithm is designed. For the UAVs’ position control, the designed formation maneuvering algorithm consists of a velocity tracking part and a formation shape control part that enables the first leader UAV to control the translational maneuvering, the second leader UAV to control the rotational and scaling maneuvering, and all the follower UAVs to maintain the formation shape. Simulations and experiments on UAVs’ formation maneuvering are conducted to illustrate the effectiveness of the proposed approach.

Weight Factor Graph Co-Location Method for UAV Formation Based on Navigation Performance Evaluation

At present, multiple unmanned aerial vehicles (UAVs) collaboration have attracted people’s more and more attention. The abundant relative distance information in UAV cooperative network can be employed to further improve UAV’s positioning accuracy. However, the traditional distance-based cooperative localization (CL) algorithm does not consider the performance of cooperative navigation information. Cooperative information with poor navigation performance will affect follower UAV’s state estimation accuracy. Thus, this paper proposes a weight factor graph CL method based on navigation performance evaluation. A novel navigation performance evaluation strategy based on Fisher information and relative entropy is proposed. The distance observations and cooperative nodes’ positions with higher estimation accuracy and contribution are selected to estimate follower UAV’s position. Considering the difference contributions of the selected cooperative information to follower UAV’s state estimation, we employ the information weight processing to achieve higher state estimation accuracy. Based on the selected cooperative information and weight coefficient, a multi-UAVs cooperative optimization graph model based on navigation performance is constructed. The global optimization cost function is constructed using relative distance constraints from the current time to historical time in the sliding window. The global optimal navigation solution of follower UAV is achieved using iterative weighted least squares method. Simulation and experiment results show that the proposed navigation performance evaluation strategy can effectively screen cooperative information with higher estimation accuracy and contribution for CL. Using the selected high-quality cooperative information, the system computational complexity is reduced without significantly affecting system positioning accuracy.

A Cooperative Localization Method for Leader–Follower Multiple UAVs Using Continuous Relative Ranging Information

Cooperative localization (CL) utilizing multiple unmanned aerial vehicles (UAVs) has become the current development trend and research hotspot. The traditional distance-based CL method typically uses principle of the spherical intersection to achieve UAV high-precision positioning. However, this distance-based CL strategy requires several leader UAVs at the known positions. Thus, a cooperative localization method for leader–follower multiple UAVs using continuous relative ranging information is proposed in this paper. Aiming at the UAV formation consists of a leader UAV node with the known position and at least a follower UAV that needs to be located. Using the Inertial short-time high-precision relative position constraint and the continuous relative distance constraints with the leader UAV nodes, the closed-form solution of follower UAV’s three-dimensional position can be obtained using spherical intersection principle. On this basis, we derive and construct follower UAV’s position observation equation. The developed strategy requires a single known position leader UAV node to realize high-precision CL, which can effectively solve the traditional distance-based collaborative location problem in case the number of leader UAVs is small. The simulation and experimental results demonstrate that the proposed method significantly improves follower UAV’s positioning accuracy, and neglects the limitation of the traditional distance-based CL method on the number of leader UAV nodes at known positions.

Federated Imitation Learning for UAV Swarm Coordination in Urban Traffic Monitoring

The popularization of unmanned aerial vehicles (UAVs) has boosted various civil applications such as traffic monitoring, in which the effective coordination of the UAV swarm plays a significant role in expanding the monitoring range and enhancing the execution efficiency. However, due to the isolated local environments as well as the heterogeneous execution capabilities, it is challenging to achieve highly consistent actions. In this article, we incorporate the federated learning framework with the imitation learning technique to coordinate the UAVs' maneuvers by interactively imitating the leader UAV's operations. During the interagent global model download phase, we utilize the generative adversarial imitation learning (GAIL) model to accurately follow the leader UAV's operations by removing the biased estimates of imitation parameters. While in the intraagent local model training phase, we utilize the self-imitation learning (SIL) model to correct delicate imitation errors by virtue of the follower UAVs' own historical valuable experiences. In order to achieve more efficient distributed parameter interactions, we regularize the federated gradient updates and eventually yield coordinated swarm policies. We evaluate the proposed algorithm in the UAV-based traffic monitoring scenario. Evaluation results demonstrate the superiorities on training and execution efficiencies.

Distributed Cooperative Control of Multiple UAVs in the Presence of Actuator Faults and Input Constraints

A distributed cooperative control algorithm of multiple unmanned aerial vehicles (UAVs) with directed topology is investigated in this brief. To overcome the actuator faults and external disturbance, incremental sliding mode formation controller is developed to realize the distributed coordination of the UAVs system, where an adaptive mechanism is utilized to deal with the chattering problem. Moreover, to reduce the adverse influence of input saturation, an auxiliary system is introduced to regulate the control signals. It is shown by Lyapunov method that the stability of the proposed algorithm can be guaranteed. A numerical example is given to illustrate that all follower UAVs can track the leader UAV.

Cooperative Location Method for Leader-Follower UAV Formation Based on Follower UAV's Moving Vector.

The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.

Distributed adaptive fault-tolerant formation control for heterogeneous multiagent systems under switching directed topologies

This paper studies the cooperative fault-tolerant formation control problem of tracking a dynamic leader for heterogeneous multiagent systems consisting of multipile unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults under switching directed interaction topologies. Based on local neighborhood formation information, the distributed fault-tolerant formation controllers are constructed to ensure that all follower UAVs and UGVs can accomplish the demanding formation configuration in the state space and track the dynamic leader’s trajectory. By incorporating the sliding mode control and adaptive control technique, the actuator faults and unknown parameters of follower agents can be compensated. Through the theoretical analysis, it is proved that the cooperatively semiglobally uniformly ultimately boundedness of the closed-loop system is guaranteed, and the formation tracking errors converge to a small adjustable neighborhood of the origin. A simulation example is introduced to show the validity of the proposed distributed fault-tolerant formation control algorithm.

Time-Varying Formation Tracking Control for Unmanned Aerial Vehicles: Theories and Applications

Time-varying formation tracking (TVFT) problems for unmanned aerial vehicles (UAVs) swarm systems with the leader’s unknown input are studied. The follower UAVs form an expected formation while tracking the leader UAV. According to the information of the neighboring UAVs, a TVFT control protocol is constructed. It’s proved by Lyapunov stability theory that the error of each follower UAV converge to a specified boundary, which means that the swarm system of UAVs can realize the practical TVFT. A platform consisting of five Tello quadrotors and ultrawideband (UWB) positioning technology is given. The effectiveness of the TVFT control protocol is verified on numerical simulation and Tello-UWB experiment platform.

Distributed Adaptive Fault-Tolerant Formation Control for Heterogeneous Multiagent Systems With Communication Link Faults

In this paper, we investigate the distributed adaptive fault-tolerant formation control problem for a group of heterogeneous multiagent systems consisting of multiple follower unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with parametric uncertainties and communication link faults. Based on the local states information of the vehicles, the adaptive fault-tolerant formation control protocol with varying control gains is developed for each follower UAV and UGV such that all followers track the dynamic trajectory of the virtual leader and obtain the expected formation configuration simultaneously under the influence of communication link faults and external disturbances. The distributed formation tracking convergence performance is discussed through Lyapunov theory. Finally, a simulation study based on the UAVs-UGVs collaborative systems is provided to show the effectiveness of the developed control strategy.

Deep Reinforcement Learning Based Three-Dimensional Area Coverage With UAV Swarm

Unmanned aerial vehicle (UAV) technology is recognized as a promising solution to area coverage problems (ACPs) and has been extensively studied recently. In this paper, we study the 3D irregular terrain surface coverage problem with a hierarchical UAV swarm. We first build the 3D model of a random irregular terrain and propose a geometric way to project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we design a coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. Moreover, an observation history model based on convolutional neural networks (CNNs) and the mean embedding method is integrated into SDQN to address the communication limitation problems of LUAVs. The numerical results show that FUAVs can cover the entire area of each patch with little redundancies, and the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithms.

Aerial Energy Orchestration for Heterogeneous UAV-Assisted Wireless Communications

The integration of unmanned aerial vehicles (UAVs) into future-generation wireless networks is a promising strategy for complementing terrestrial communication infrastructures that will lead to capacity and coverage improvements. Unfortunately, the energy limitations of the existing off-the-shelf small-size UAVs are significant challenges, especially during long missions. In this article, first, we investigate the communication model of a heterogeneous UAV-assisted wireless network that consists of multiple energy-constrained battery-powered small-size UAVs (a.k.a. follower UAVs) and a large-size UAV (a.k.a. dispatcher UAV) with solar energy harvesting capability. Subsequently, we propose an aerial energy sharing algorithm to improve the lifetime of the system by orchestrating energy balancing among follower UAVs. In particular, the dispatcher UAV having large energy capacity owing to its battery size and solar energy harvesting capability shares its energy with follower UAVs using radio-frequency-based power transfer through beamforming. The proposed energy sharing algorithm considers the placement, available battery capacity, and mission execution time of the follower UAVs to decide the energy sharing points with optimum efficiency. Extensive simulations show that our proposed algorithm outperforms existing algorithms in terms of charging efficiency and energy balancing among UAVs.

Fault-Tolerant Formation Tracking Control for Heterogeneous Multiagent Systems with Directed Topology

In this paper, the adaptive fault-tolerant formation tracking control problem for a set of heterogeneous unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) systems with actuator loss of effectiveness faults is investigated. The cooperative fault-tolerant formation control strategy for UAV and UGV collaborative systems is classified into the altitude consensus control scheme for follower UAVs and the position cooperative formation control scheme for all followers. The altitude consensus control algorithm is designed by utilizing backstepping control technique to drive all UAVs to a desired predefined height. Then, based on synchronization formation error information, the position cooperative formation control algorithm is proposed for all followers to reach the expected position and perform the desired formation configuration. The adaptive fault estimation term is adopted in the designed fault-tolerant formation control algorithm to compensate for the actuator loss of effectiveness fault. Finally, a simulation example is proposed to reveal the validity of the designed cooperative formation tracking control scheme.

Fully Distributed Cooperative Circumnavigation of Networked Unmanned Aerial Vehicles

In this article, cooperative circumnavigation (CCN) is studied for groups of networked unmanned aerial vehicles (UAVs) under a directed interaction topology. The CCN drives UAVs to given planar ellipses with desired spatial formation. A first contribution is that based on affine transformations, CCN control design is structured, which can deploy UAVs on spatial orbits with different radii concerning a moving target in 3-D space. Second, no global information, such as the formation center, the desired radius, the angular velocity of circumnavigation, etc., is needed for any follower UAVs since they are completely maneuvered by the leader UAVs.

Adaptive leader–follower formation control for swarms of unmanned aerial vehicles with motion constraints and unknown disturbances

In this paper, the 3D leader–follower formation control problem, which focuses on swarms of fixed-wing Unmanned Aerial Vehicles (UAVs) with motion constraints and disturbances, has been investigated. Original formation errors of the follower UAVs have been transformed into the Frenet-Serret frame. Formation control laws satisfying five motion constraints (i.e., linear velocity, linear acceleration, heading rate, climb rate and climb angle) have been designed. The convergence of the control laws has been discussed via the Lyapunov stability tool. In addition, to address the unknown disturbances, an adaptive disturbance observer is exploited. Furthermore, formation control laws involving estimated disturbances are presented as well. The collision avoidance between UAVs is achieved with the artificial potential method. Simulation results obtained using four scenarios verify the effectiveness of the proposed method in situations with constant disturbances and varying disturbances, as well as without disturbances.

Distributed sliding mode control for leader‐follower formation flight of fixed‐wing unmanned aerial vehicles subject to velocity constraints

SummaryThis paper considers the leader‐follower formation flight of fixed‐wing unmanned aerial vehicles (UAVs) subject to velocity constraints. A novel distributed sliding mode control law is proposed for each UAV, whose kinematics is described by a unicycle model with a saturated angular velocity and a bounded linear velocity within an interval. The designed control law of each follower UAV only uses its own information and the information of its leader UAV. Driven by the designed control law, the desired formation is achieved with rigorous proof, while the follower UAVs' constraints of both the linear and angular velocities are satisfied. Moreover, the follower's speed adjustment range is relaxed and not required to be strictly larger than their leaders'. Finally, numerical simulations are presented to verify the results.

Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach

This paper investigates a formation control problem of fixed-wing Unmanned Aerial Vehicle (UAV) swarms. A group-based hierarchical architecture is established among the UAVs, which decomposes all the UAVs into several distinct and non-overlapping groups. In each group, the UAVs form hierarchies with one UAV selected as the group leader. All group leaders execute coordinated path following to cooperatively handle the mission process among different groups, and the remaining followers track their direct leaders to achieve the inner-group coordination. More specifically, for a group leader, a virtual target moving along its desired path is assigned for the UAV, and an updating law is proposed to coordinate all the group leaders’ virtual targets; for a follower UAV, the distributed leader-following formation control law is proposed to make the follower’s heading angle coincide with its direct leader, while keeping the desired relative position with respect to its direct leader. The proposed control law guarantees the globally asymptotic stability of the whole closed-loop swarm system under the control input constraints of fixed-wing UAVs. Theoretical proofs and numerical simulations are provided, which corroborate the effectiveness of the proposed method.

Vertically Optimal Close Formation Flight Control Based on Wingtip Vortex Structure

In this paper, aiming to drive the unmanned aerial vehicle (UAV) to the optimal position of the vertical plane in the close formation flight, a control method that is based on extended state observer (ESO) and wingtip vortex structure is proposed. This method is suitable for cases where the optimal position is unknown and there are gusts. The scheme is divided into three parts. In the first part, an ESO is designed to estimate the change of the lift and other external disturbances on the follower UAV. In the second part, an algorithm making use of the estimates of the change of the lift to predict the optimal position for the follower UAV is proposed. In the third part, to stabilize the UAV around the optimal position, a sliding mode controller is designed and the external disturbances on the follower UAV are compensated by the ESO. The simulation results show that the followers could automatically adjust their spatial positions to track the predicted optimal positions behind the leader UAV and achieve the precisely close formation flight without the information of leader’s altitude.