Formation Control Of Unmanned Aerial Vehicles Research Articles

Distributed formation control of multi-UAV systems using relative information

An adaptive distributed control strategy is designed for the formation of multiple unmanned aerial vehicle (UAV) groups by using only relative measurement information. A distributed estimator is first designed to get the estimate of the consensus error. Then, an adaptive distributed controller is proposed to realize the formation tracking with the relative measurements. Based on the position and velocity information, a control barrier function (CBF) algorithm is applied to achieve the UAV formation under safe position constraints. In addition, in order to break up and reorganize multiple UAV groups, the proposed controller is designed based on the time-varying network topology. It is proved that the closed-loop system is uniformly asymptotically stability. Simulation and experiment results indicate that the proposed strategy can achieve formation control of multiple UAVs under safety constraints based on the measurement of relative measurement information.

UAV formation control using enhanced behavior mechanism and artificial potential field

Inspired by formation flight of pigeon flock, this paper proposes a enhanced method of autonomous formation control of multiple Unmanned Aerial Vehicles (UAVs) that can maintain high symmetry based on pigeon flock behavior mechanism. Addressing the instability of formation in the original method, the follow improvements have been made. Firstly, improve leadership of top three UAVs, Secondly, modify artificial potential field strategies for top two followers. Finally, through a series of simulation experiments, it is verified that the UAVs can form the expected formation under the autonomous formation control, and can maintain the formation under the complex motion of leader UAV.

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

This article addresses the leader-follower formation maneuver control problem of multiple unmanned aerial vehicles (UAVs), taking into account the time-varying velocity and time-varying relative bearing. An event-triggered bearing-based distributed velocity observer was designed, using only the desired position and velocity of the leaders. Furthermore, a dynamic event-triggered mechanism was introduced to reduce continuous communication between UAVs, thus effectively saving communication bandwidth and resources. Building on this, a bearing-only formation maneuver control strategy was proposed, integrating the event-triggered velocity observer with the backstepping control approach. To conclude, numerical simulations have been conducted to confirm the effectiveness of the proposed scheme in accomplishing formation maneuver control objectives, including translation, scaling, and rotation control. Furthermore, the advantages of the dynamic event-triggering strategy have been demonstrated through comparative simulations with traditional event-triggering strategies. Additionally, the effectiveness of the proposed observer and controller has been demonstrated by a comprehensive hardware-in-the-loop (HITL) simulation example.

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.

Fixed-Time Collision-Free Fault-Tolerant Formation Control of Multi-UAVs Under Actuator Faults.

When cooperating through an intensive formation, the safe distancing of unmanned aerial vehicles (UAVs) is a delicate issue, especially if UAVs are subjected to actuator faults that cause rapid maneuvers. This article investigates the fixed-time fault-tolerant formation control of multiple quadrotor UAVs under actuator faults, which considers the collision avoidance among UAVs when faults occur, and the convenience of engineering application. First, an augmented fixed-time observer with measurement noise oppression is adopted to estimate and compensate actuator faults and disturbance in rotational and translational dynamics. Then, a baseline attitude controller, a command filter, and a velocity controller are proposed for each quadrotor UAV to track the desired velocity within a fixed time. Next, a distributed fixed-time sliding-mode controller that integrates the gradient of repulsive potential function into the sliding manifold is designed to achieve leader-follower formation control and collision avoidance simultaneously. The control scheme is proven to be fixed-time convergent via Lyapunov stability analysis and is normalized in accordance with the compatibility of hardware implementation. Finally, the designed algorithm is embedded into PX4 architecture to illustrate the effectiveness and practicality of the control strategy.

Formation control of fixed-wing UAVs with communication delay

Fixed-wing unmanned aerial vehicles (UAVs) possess high speed and non-hovering capabilities, rendering them uniquely advantages for reconnaissance and detection. The focus of this paper is to addressing the problem of formation control for fixed-wing UAVs in the presence of communication delay. To tackle this problem, for the non-holonomic kinematic model, we propose an intuitive and practical control law based on the leader-follower method to ensure that UAVs maintain a predetermined geometric formation. The stability analysis of the system with communication delay is conducted by constructing a strict Lyapunov–Krasovskii function. Furthermore, we consider the impact of communication delay on formation accuracy and present a prediction algorithm capable of forecasting the actual position of each UAV. To validate our theoretical findings, both digital simulation and hardware-in-loop experiment are conducted.

Stylized Crowd Formation Transformation Through Spatiotemporal Adversarial Learning

Achieving crowd formation transformations has wide‐ranging applications in fields such as unmanned aerial vehicle formation control, crowd simulation, and large‐scale performances. However, planning trajectories for hundreds of agents is a challenging and tedious task. When modifying crowd formation change schemes, adjustments are typically required based on the style of formation change. Existing methods often involve manual adjustments at each crucial step, leading to a substantial amount of physical labor. Motivated by these challenges, this study introduces a novel generative adversarial network (GAN) for generating crowd formation transformations. The proposed GAN learns specific styles from a series of crowd formation transformation trajectories and can transform a new crowd with an arbitrary number of individuals into the same styles with minimal manual intervention. The model incorporates a space–time transformer module to aggregate spatiotemporal information for learning distinct styles of formation transformation. Furthermore, this article investigates the relationship between the distribution of training data and the length of trajectory sequences, providing insights into the preprocessing of training data.

Reconfiguration for UAV Formation: A Novel Method Based on Modified Artificial Bee Colony Algorithm

The flight formation of unmanned aerial vehicles (UAVs) needs to be reconfigured whenever necessary to cope with complex environments and varying tasks. However, the continuity, nonlinearity and high dimensionality of the UAV formation control parameters bring significant challenges to the efficiency and safety of UAV formation reconfiguration. To this end, this paper proposes a reconfiguration strategy of the UAV formation based on a modified Artificial Bee Colony (ABC) algorithm, which ensures superior efficiency and safety level simultaneously. Specifically, we first formulate the formation reconfiguration problem minimizing the time consumed for reconfiguration under the constraints of safety and connection. Then the continuous optimization problem is discretized by using the control parameterization and time discretization (CPTD) method. Finally, we use a modified ABC algorithm to find the solution of formation reconfiguration. Extensive performance evaluations are conducted to verify the superiority of the proposed method. It is concluded that the proposed algorithm achieves a better performance than the existing approaches in literature in solving the problem of 3-D formation reconfiguration.

Delay-Informed Intelligent Formation Control for UAV-Assisted IoT Application.

Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader-follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader's trajectory. This paper studies the leader-follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays.

Formation Control for Mixed-Order UAVs–USVs–UUVs Systems under Cooperative and Optimal Control

In this paper, cooperative control and optimal control methods are used for the formation control of mixed-order heterogeneous multi-agent systems. The system consists of unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs). The system is represented in a state space using a block Kronecker product. The static and dynamic formation control protocols are proposed respectively, and the graph theory is used to prove that formation control protocols can realize system formation. Furthermore, the optimal control and cooperative control are introduced into the static and the dynamic formation control protocols, and the static cooperative optimal formation control protocol and the dynamic cooperative optimal formation control protocol are designed. Through MATLAB simulation, the static cooperative optimal control protocol and static formation control protocol are compared, and the dynamic cooperative optimal control protocol and dynamic formation control protocol are compared. By comparison, the state variables of the system can reach convergence quickly, and the system can complete formation in a short time, which verifies the effectiveness of the optimal theory and cooperative control.

Nonlinear tight formation control of multiple UAVs based on model predictive control

A tight formation of unmanned aerial vehicles (UAVs) has many advantages, such as fuel saving and deceiving enemy radar during battlefield entry. As a result, research on UAVs in close formation has received much attention, and the controller design for formation holding has become a popular research topic in the control field. However, there are many unknown disturbances in tight formation, and the tail aircraft is disturbed by the wake. This paper establishes a mathematical model of wake vortices for tail aircraft that considers uncertainty and strong interference. Two UAVs are simulated by Computational Fluid Dynamics software, followed by the design of a semiphysical simulation model predictive control (MPC) scheme that suppresses uncertainty and interference sufficiently to enable the tail aircraft to accurately track the lead aircraft and maintain a stable, tight formation. The tight formation controller is verified by numerical simulation and semiphysical simulation. The results show that the designed controller has an excellent control effect in the case of disturbance caused by the wake vortex.

Dynamic event-triggered formation control for UAVs under communication delay and outside disturbances

This work presents a dynamic event-triggered sliding mode controller to solve the problem on cooperative formation flight for unmanned aerial vehicles (UAVs) under outside disturbances and communication delay. Initially, a second-order kinematic model for each UAV is established, and the leader-follower framework is adopted to realize the desired formation flight. Second, an effective design of disturbance observer is proposed based on that the disturbance in the UAV is generated by an external nonlinear system. Third, a new dynamic event-triggered mechanism (DETM) is presented and the triggering threshold is adjusted by an exogenous system, which can help reduce data transmissions and maintain control performance more efficiently. Then, a distributed dynamic event-triggered sliding mode control strategy is proposed for the UAV system, and two sufficient conditions on exponential stability of the overall closed-loop system are derived. Furthermore, the controller gain, observer gain, and triggering parameter can be obtained by solving the derived linear matrix inequalities (LMIs). Finally, a simulated example is exploited to illustrate the obtained results.

Decentralized modular hybrid supervisory control for the formation of unmanned helicopters

Formation control of Unmanned Aerial Vehicles (UAVs) requires them to tightly cooperate to reach and keep the formation, while avoiding collision. This paper proposes a novel decentralized hybrid supervisory control approach for the formation control of multiple UAVs. This is achieved by developing a symbolic motion planning technique to polarly partition the motion space resulting in a finite state discrete event model for the motion dynamics of each UAV. Then, a modular discrete supervisor is designed for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Further, for the collision avoidance mechanism, a novel top-down decomposition-based approach is developed to design local supervisors decentralizedly. It is formally proved that with the proposed top-down decomposition-based approach, the (locally) supervised UAVs, as a whole, can cooperatively satisfy the desired (global) collision avoidance specification. The proposed decentralized supervisory control algorithm is also verified through a hardware-in-the-loop simulator for the formation control of unmanned helicopters.

Robust horizontal-plane formation control for small fixed-wing UAVs

The formation control problems of small fixed-wing(FW) unmanned aerial vehicles (UAVs) are investigated in this paper. By adopting a simplified aerodynamic force model and a cascade control structure, the horizontal motion of a FW UAV can be described as a linear system with uncertainties based on the feedback linearization technique. Thereafter, a novel robust formation protocol is proposed, which incorporates two components: the nominal controller and the robust filter-based compensator to restrain the influences of equivalent uncertainties. It turns out the formation error can converge to a sufficiently small neighborhood of the origin. The stability of the closed-loop system is proved in combination with classic Lyapunov-based arguments. Finally, field formation flight experiments on four small FW UAVs are carried out to demonstrate the effectiveness of the theoretical results.

Fully distributed prescribed performance formation control for UAVs with unknown maneuver of leader

This paper focuses on the formation control for multiple quadrotor unmanned aerial vehicles (UAVs) in a leader-follower structure. Taking account of the unknown maneuver of the leader and external disturbances, a novel fully distributed prescribed performance formation controller is proposed. Firstly, to obtain a quick convergence rate of errors and desired control precision, a performance function is introduced into a time-varying barrier Lyapunov function (TV-BLF). Then, a distributed adaptive extended state observer (AESO) is constructed to estimate the maneuver of the leader and external disturbances simultaneously. A distributed formation controller is established further based on the TV-BLF and estimations of the AESO. It is proved rigorously that by using the proposed formation controller, the desired formation pattern can be formed while the prescribed performance is guaranteed. Moreover, no global topology information is required in the proposed AESO and controller, which means that the formation can be realized in a fully distributed way. Last, simulations are performed to validate the effectiveness and the superiority of the presented scheme.

Bearing-Based Distributed Formation Control of Unmanned Aerial Vehicle Swarm by Quaternion-Based Attitude Synchronization in Three-Dimensional Space

Most of the recent research on distributed formation control of unmanned aerial vehicle (UAV) swarms is founded on position, distance, and displacement-based approaches; however, a very promising approach, i.e., bearing-based formation control, is still in its infancy and needs extensive research effort. In formation control problems of UAVs, Euler angles are mostly used for orientation calculation, but Euler angles are susceptible to singularities, limiting their use in practical applications. This paper proposed an effective method for time-varying velocity and orientation leader agents for distributed bearing-based formation control of quadcopter UAVs in three-dimensional space. It combines bearing-based formation control and quaternion-based attitude control using undirected graph topology between agents without the knowledge of global position and orientation. The performance validation of the control scheme was done with numerical simulations, which depicted that UAV formation achieved the desired geometric pattern, translation, scaling, and rotation in 3D space dynamically.

UAV formation control based on distributed Kalman model predictive control algorithm

To address the perturbation of formation of multiple unmanned aerial vehicles (UAVs) subject to external disturbances, an algorithm of distributed Kalman model predictive control is proposed in this paper to improve the accuracy of maintaining a formation in flight. A UAV two-order discrete-time system model was built before devising a Kalman prediction model based on the standard prediction model. The desired formation configuration and neighbor Kalman optimal state estimation were conducted to determine the reference state of UAVs. While taking into account the formation tracking error and input stability, a logarithmic barrier function was introduced in the design of the overall cost function to ensure flight safety. Meanwhile, information was exchanged with neighbors with the directed and time-invariant communication topological structure. With the Lyapunov stability theorem, sufficient conditions were defined for the asymptotic stability of the formation system. Simulation results revealed that the algorithm could effectively suppress the perturbation in the formation of UAVs arising from external disturbances, allowing the formation to cope with the conflicts between individual UAVs.

Formation control for UAVs using a Flux Guided approach

Existing studies on formation control for unmanned aerial vehicles (UAV) have not considered encircling targets where an optimum coverage of the target is required at all times. Such coverage plays a critical role in many real-world applications such as tracking hostile UAVs. This paper proposes a new path planning approach called the Flux Guided (FG) method, which generates collision-free trajectories for multiple UAVs while maximising the coverage of target(s). Our method enables UAVs to track directly toward a target whilst maintaining maximum coverage. Furthermore, multiple scattered targets can be tracked by scaling the formation during flight. FG is highly scalable since it only requires communication between sub-set of UAVs on the open boundary of the formation’s surface. Experimental results further validate that FG generates UAV trajectories 1.5× shorter than previous work and that trajectory planning for 9 leader/follower UAVs to surround a target in two different scenarios only requires 0.52 s and 0.88 s, respectively. The resulting trajectories are suitable for robotic controls after time-optimal parameterisation; we demonstrate this using a 3d dynamic particle system that tracks the desired trajectories using a PID controller.

An Improved Multi-Objective Cuckoo Search Approach by Exploring the Balance between Development and Exploration

In recent years, multi-objective cuckoo search (MOCS) has been widely used to settle the multi-objective (MOP) optimization issue. However, some drawbacks still exist that hinder the further development of the MOCS, such as lower convergence accuracy and weaker efficiency. An improved MOCS (IMOCS) is proposed in this manuscript by investigating the balance between development and exploration to obtain more accurate solutions while solving the MOP. The main contributions of the IMOCS could be separated into two aspects. Firstly, a dynamic adjustment is utilized to enhance the efficiency of searching non-dominated solutions in different periods utilizing the Levy flight. Secondly, a reconstructed local dynamic search mechanism and disturbance strategy are employed to strengthen the accuracy while searching non-dominated solutions and to prevent local stagnation when solving complex problems. Two experiments are implemented from different aspects to verify the performance of the IMOCS. Firstly, seven different multi-objective problems are optimized using three typical approaches, and some statistical methods are used to analyze the experimental results. Secondly, the IMOCS is applied to the obstacle avoidance problem of multiple unmanned aerial vehicles (UAVs), for seeking a safe route through optimizing the coordinated formation control of UAVs to ensure the horizontal airspeed, yaw angle, altitude, and altitude rate are converged to the expected level within a given time. The experimental results illustrate that the IMOCS can make the multiple UAVs converge in a shorter time than other comparison algorithms. The above two experimental results indicate that the proposed IMOCS is superior to other algorithms in convergence and diversity.