Multiple Unmanned Aerial Vehicle Systems Research Articles

Adaptive neural network based quadrotor UAV formation control under external disturbances

The formation control of a team comprised of multiple quadrotor Unmanned Aerial Vehicles (UAVs) may severely be affected by the unknown external disturbances. The external disturbances are caused by wind forces to create aero-dynamical disturbances. This article addresses the robust formation control problem of multiple UAVs system despite the effect of external disturbances that allow sustaining a stable network connection among the UAVs and maintaining different formations assigned to them. First, a Radial Basis Function Neural Network (RBFNN) based model is developed to reciprocate the external disturbances along the positional and the attitude subsystems. Then incorporating the estimated disturbance values a distributed adaptive formation controller is devised using the Lyapunov theory. It consists of a positional and an attitude controller associated with the translational and the rotational movements of the UAVs. The stability is validated by satisfying the criteria of the Lyapunov stability function. The UAVs are connected through variable adjacency matrix based directed network topology and the network connectivity is established through the properties of the Laplacian Matrix. The robustness of the designed controller is justified via rigorous simulation studies for different sets of desired formations such as triangular, squared, tetrahedron, octahedron and cube shaped. The reference trajectories are considered as spiral, straight line and circular shaped. The time varying external disturbances are considered of sinusoidal waveform of different magnitudes. The simulation results signifies that the proposed RBFNN based formation controller reciprocate different sinusoidal waveforms to achieve the desired formations successfully. Extensive comparative studies demonstrate the efficacy of the proposed adaptive formation controller over the existing controllers presented in the literature for different shapes of trajectories and desired formations.

Multi UAV Cooperative Reconnaissance based on Dynamic Programming VDN Algorithm

This paper proposes a multi agent value decomposition network (VDN) based multi UAV collaborative reconnaissance and control method to address the issue of insufficient strategies for multi UAV collaborative reconnaissance and control. By designing corresponding algorithm networks and training processes, the goal of autonomy, collaboration, and intelligence among multiple unmanned aerial vehicle systems has been achieved, assisting unmanned aerial vehicle combat forces in achieving collaborative operations and decision-making. This article uses AirSim as the simulation verification environment to verify the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed in this paper can achieve multi UAV collaborative reconnaissance tasks in complex environments, providing an intelligent solution for UAV collaborative control.

A multi‐UAV system for coverage path planning applications with in‐flight re‐planning capabilities

AbstractThis paper presents the development and implementation of a multiple unmanned aerial vehicle system focused on coverage path planning on multiple separated areas capable of re‐planning the collective mission in case of unexpected events. For this purpose, we present a distributed‐centralized architecture that uses heuristic and computationally efficient methods to perform the planning/re‐planning and decision‐making tasks during the control of the mission execution. We performed a computational evaluation of the algorithms, comparing them with other proposals, together with experiments in simulated and real flights. The results show that the system can distribute tasks equitably among the aircraft in an efficient way, even in the middle of the flight, when facing unexpected events; and show a higher computational efficiency when compared to multiple proposals in the state of the art.

Time-Coordinated Path Following for Multiple Agile Fixed-Wing UAVs with End-Roll Expectations

This paper presents a control strategy for multiple unmanned aerial vehicle systems (multi-UAVs) time-coordinated path following with desired endpoint roll attitudes. It utilizes the strong maneuvering capabilities of agile fixed-wing UAVs and incorporates an end-roll expectation. The strategy consists of four steps: time-coordinated control, position control, roll angle planning and attitude control. The position and attitude controllers exhibit Lyapunov exponential stability. The time-coordinated controller addresses the synchronization problem by adjusting the speed based on the coordinated state to achieve progress adjustment. The position controller operates based on the cross-track error and altitude error in the Gravity-Referenced Moving frame. By employing an optimization approach and designing a penalty function, the roll angle sequence is computed. The attitude inner-loop control operates in the [Formula: see text] space and allows for control of large deviations. High-fidelity simulation validates the effectiveness of the proposed method, with normalized coordination error and following error controlled within 2% and 1.2[Formula: see text]m.

A Study on Leveraging Unmanned Aerial Vehicle Collaborative Driving and Aerial Photography Systems to Improve the Accuracy of Crop Phenotyping

Unmanned aerial vehicle (UAV)-based aerial images have enabled a prediction of various factors that affect crop growth. However, the single UAV system leaves much to be desired; the time lag between images affects the accuracy of crop information, lowers the image registration quality and a maximum flight time of 20–25 min, and limits the mission coverage. A multiple UAV system developed from our previous study was used to resolve the problems centered on image registration, battery duration and to improve the accuracy of crop phenotyping. The system can generate flight routes, perform synchronous flying, and ensure capturing and safety protocol. Artificial paddy plants were used to evaluate the multiple UAV system based on leaf area index (LAI) and crop height measurements. The multiple UAV system exhibited lower error rates on average than the single UAV system, with 13.535% (without wind effects) and 17.729–19.693% (with wind effects) for LAI measurements and 5.714% (without wind effect) and 4.418% (with wind effects) for crop’s height measurements. Moreover, the multiple UAV system reduced the flight time by 66%, demonstrating its ability to overcome battery-related barriers. The developed multiple UAV collaborative system has enormous potential to improve crop growth monitoring by addressing long flight time and low-quality phenotyping issues.

Denial of Service Attack of QoS-Based Control of Multi-Agent Systems

This paper presents a secure formation control design of multi-agent systems under denial of service (DoS) attacks. Multiple unmanned aerial vehicle systems (UAVs) are considered in this paper. The proposed technique takes into account communication time delay, as well as formation and cyberattack, and provides a robust guidance method as well as a reliable middleware for information transfer and sharing. To ensure optimal guidance and coordination, a combined approach of L1 adaptive control and graph theory is used. The packet transmission between all UAVs is handled by the data distribution services (DDS) middleware, which overcomes the interoperability problem when dealing with multiple UAVs of different platforms and can be considered as an extra security level based on its quality of service (QoS). The graph theory is utilized to coordinate multiple UAVs in a hexagon formation, while the L1 controller is utilized as a local controller to stabilize the UAV’s dynamic model. A robust control security level is built to handle the effect of cyberattacks based on linear matrix inequalities (LMIs) control. Simulations are used to verify and show the performances of the proposed technique under the conditions indicated earlier.

Finite-Time Attitude Cooperative Control of Multiple Unmanned Aerial Vehicles via Fast Nonsingular Terminal Sliding Mode Control

This paper discusses the attitude cooperative control of multiple unmanned aerial vehicle systems (MUAVs) with unknown dynamics and external disturbances. Distributed fast nonsingular terminal sliding mode control (FNTSMC) is used with quaternion-description dynamical systems. The dynamics and external disturbances are changed into lumped disturbances by formula transformation. A robust nonlinear disturbance observer is proposed to estimate the lumped disturbances in finite time. Then, combining the fast terminal sliding mode control, distributed FNTSMC controllers are designed under the directed topology. Based on the Lyapunov stability theory and graph theory, convergence stability of the nonlinear systems is strictly proved, and the tracking errors between the leader and the followers approach to a small residual set. Finally, the simulation example is presented to illustrate the effectiveness and advantage of the proposed controllers.

Event-Triggered Finite-Time Attitude Cooperative Control for Multiple Unmanned Aerial Vehicles.

The finite-time attitude cooperative control problem for a group of multiple unmanned aerial vehicle systems with external disturbances and uncertain parameters is discussed in this paper. The dynamics of the systems is described by quaternion avoiding the singularity. Based on the attitude error and angular velocity error, a novel nonsingular terminal sliding mode surface is proposed for the controller with event-triggered scheme. The lumped disturbances are estimated by neural networks with adaptive law. The communication frequency is decreased by the proposed distributed event-triggered based sliding mode controller. Lyapunov theory is utilized to analyze the stability of the systems, and the Zeno behavior is avoided by rigorous proof. Finally, simulation examples are presented to illustrate the efficiency of the proposed control algorithm.

Taking FANET to Next Level

Flying Ad-hoc Network (FANET) is a special member/class of Mobile Ad-hoc Network (MANET) in which the movable nodes are known as by the name of Unmanned Aerial Vehicles (UAVs) that are operated from a long remote distance in which there is no human personnel involved. It is an ad-hoc network in which the UAVs can more in 3D ways simultaneously in the air without any onboard pilot. In other words, this is a pilot free ad-hoc network also known as Unmanned Aerial System (UAS) and the component introduced for such a system is known as UAV. There are many single UAV applications but using multiple UAVs system cooperating can be helpful in many ways in the field of wireless communication. Deployments of these small UAVs are quick and flexible which overcome the limitation of traditional ad hoc networks. FANETs differ from other kinds of ad hoc networks and envisioned to play an important role where infrastructure operations are not available and assigned tasks are too dull, dirty, or dangerous for humans. Moreover, setting up to bolster the range and performance of small UAV in ad hoc network lead to emergent evolution with its high stability, quick deployment, and ease-of-use for the formation of the network. Routing and task allocation are the challenging research areas of the network with ad hoc nodes. The paper overview based on the study of biological inspired routing protocols (Moth-and-Ant and Bee Ad-Hoc) routing protocols.

Time‐varying formation of second‐order discrete‐time multi‐agent systems under non‐uniform communication delays and switching topology with application to UAV formation flying

In this study, the time-varying formation control problem for the second-order discrete-time multi-agent systems is investigated, where both the non-uniform communication time-delays and switching topology are taken into account. A linear discrete-time formation protocol is developed based on the neighbouring relative position and velocity information. Using the state transformation method and properties of the stochastic matrix, the formation feasibility condition is given and sufficient conditions for the discrete-time multi-agent systems to accomplish the time-varying formation are established. An unmanned aerial vehicles (UAVs) formation experiment platform is constructed. Using four quadrotor UAVs, UAV formation flying experiments are performed to verify the effectiveness and reliability of the discrete-time formation protocol. The experimental results show that the theoretical results can be used to deal with the time-varying formation control problem for multiple UAVs system with communication delays and switching topology.

Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approach

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

Formation Control of Multiple UAVs Incorporating Extended State Observer-Based Model Predictive Approach

This paper studies the extended state observer-based state space predictive control approach to deal with the multiple unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the state space predictive control approach based on an error model to achieve stable tracking. Then, a feedforward compensation controller is introduced by using the extended state observer to estimate and compensate disturbances and improve the ability of anti-interference. Besides, the bounded stability of the designed extended state observer is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of the proposed control structure.

A distributed swarm control for an agricultural multiple unmanned aerial vehicle system

In this study, we propose a distributed swarm control algorithm for an agricultural multiple unmanned aerial vehicle system that enables a single operator to remotely control a multi-unmanned aerial vehicle system. The system has two control layers that consist of a teleoperation layer through which the operator inputs teleoperation commands via a haptic device and an unmanned aerial vehicle control layer through which the motion of unmanned aerial vehicles is controlled by a distributed swarm control algorithm. In the teleoperation layer, the operator controls the desired velocity of the unmanned aerial vehicle by manipulating the haptic device and simultaneously receives the haptic feedback. In the unmanned aerial vehicle control layer, the distributed swarm control consists of the following three control inputs: (1) velocity control of the unmanned aerial vehicle by a teleoperation command, (2) formation control to obtain the desired formation, and (3) collision avoidance control to avoid obstacles. The three controls are input to each unmanned aerial vehicle for the distributed system. The proposed algorithm is implemented in the dynamic simulator using robot operating system and Gazebo, and experimental results using four quadrotor-type unmanned aerial vehicles are presented to evaluate and verify the algorithm.

Dynamic Control Scheme of Multiswarm Persistent Surveillance in a Changing Environment.

The persistent surveillance problem has been proved to be an NP hard problem for multiple unmanned aerial vehicle systems (UAVs). However, most studies in multiple UAV control focus on control cooperative path planning in a single swarm, while dynamic deployment of a multiswarm system is neglected. This paper proposes a collective control scheme to drive a multiswarm UAVs system to spread out over a time-sensible environment to provide persistent adaptive sensor coverage in event-related surveillance scenarios. We design the digital turf model to approximate the mixture information of mission requirements and surveillance reward. Moreover, we design a data clustering-based algorithm for the dynamic assignment of UAV swarms, which can promote workload balance, while also allowing real-time response to emergencies. Finally, we evaluate the proposed architecture by means of simulation and find that our method is superior to the conventional control strategy in terms of detection efficiency and subswarm equilibrium degree.

Time-optimal control of multiple unmanned aerial vehicles with human control input

PurposeThe purpose of this paper is to investigate time-optimal control problems for multiple unmanned aerial vehicle (UAV) systems to achieve predefined flying shape.Design/methodology/approachTwo 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.FindingsThe 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.Originality/valueThis paper proposes a solution to solve the time-optimal control problems for multiple UAV systems to achieve predefined flying shape.

Adaptive finite‐time formation tracking control for multiple nonholonomic UAV system with uncertainties and quantized input

SummaryAn adaptive finite‐time formation tracking control approach is proposed for multiple unmanned aerial vehicle (UAV) system with quantized input signals in this paper. The UAVs are described by nonholonomic kinematic model and autopilot model with uncertainties. An enhanced hysteretic quantizer is introduced to avoid chattering, and some restrictions are released by using a new quantization decomposition method. Based on backstepping technique and finite‐time Lyapunov stability theory, the adaptive finite‐time controller is designed for the trajectory tracking of the multi‐UAV formation. The nonholonomic constraints are solved by a transverse function. A transformation is introduced to the control input signals to eliminate the quantization effect. Stability analysis proves that the tracking errors can converge to a small neighborhood of the origin within finite time and all the closed‐loop signals are semiglobally finite‐time bounded. The effectiveness of the proposed control approach is validated by simulation and experiment.

Multiple UAV Systems for Agricultural Applications: Control, Implementation, and Evaluation

The introduction of multiple unmanned aerial vehicle (UAV) systems into agriculture causes an increase in work efficiency and a decrease in operator fatigue. However, systems that are commonly used in agriculture perform tasks using a single UAV with a centralized controller. In this study, we develop a multi-UAV system for agriculture using the distributed swarm control algorithm and evaluate the performance of the system. The performance of the proposed agricultural multi-UAV system is quantitatively evaluated and analyzed through four experimental cases: single UAV with autonomous control, multiple UAVs with autonomous control, single UAV with remote control, and multiple UAVs with remote control. Moreover, the performance of each system was analyzed through seven performance metrics: total time, setup time, flight time, battery consumption, inaccuracy of land, haptic control effort, and coverage ratio. Experimental results indicate that the performance of the multi-UAV system is significantly superior to the single-UAV system.

Trajectory Tracking in the Desired Formation around a Target by Multiple UAV Systems

In this work, a target-centric formation of multiple Unmanned Aerial Vehicle (UAV) system is presented based on the Kalman filter. Network connectivity among the UAVs is essential for sharing information during real-time implementation. A framework to recognize such network connectivity is incorporated by considering the Eigen value of the Laplacian matrix of the UAVs in formation. Chasing and covering of targets by a number of UAVs are depicted through simulation results. The network connectivity among the UAVs in the formation is also presented to show the applicability of this work to different cases of a multi-UAV formation control.

Time-varying formation finite-time tracking control for multi-UAV systems under jointly connected topologies

PurposeThe 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.Design/methodology/approachA 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.FindingsThe 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.Originality/valueThis paper proposes a solution to simultaneously solve the control problems of time-varying formation tracking, finite-time convergence, and switching topologies.

UAVs flocking and reconfiguration control based on artificial physics

This paper investigates the flocking and reconfiguration control problem of multiple unmanned aerial vehicles (UAVs) system by using a modified artificial physics (AP) method. Firstly, in order to dirve the multi-UAV system with double-intergrator dynamics to form the uniform distribued standard formation, we redefine the attractive and repulsive forces in the traditional artificial physics method, and analyze the stability of the flocking control method using the LaSalle invariance principle accordingly. Immediately, we develop an improved strategy regards to the local optimal solution of the flocking problem in the serial number independent case. Then, we explore the reversible and exclusive bijective transformation between the standard formation and an arbitrary formation. Using the bijective transformation, we propose a flocking control method to form the arbitrary formation, and futher extend to the flocking reconfiguration problem, such as flocking contraction and expansion, formation switching and flocking with obstacle avoidence. In order to verify the effectiveness of the proposed flocking and reconfiguration control method, we construct a multiple quadrotors coodination simulation platform based on the robot operating system (ROS) and Gazebo simulator. The simulation results demonstrate the effectiveness of the flocking and reconfiguration control method. Finally, we expand our artificial physics based flocking control method to the multiple fixed-wing UAVs system, and the effectiveness is verified in a semi-physical simulation environment for multi-UAV coodination by using X-Plane flight simulaor and the UAV autopilot.