Elliptical encirclement control capable of reinforcing performances for UAVs around a dynamic target

Most researches associated with target encircling control are focused on moving along a circular orbit under an ideal environment free from external disturbances.However,elliptical encirclement with a time-varying observation radius,may permit a more flexible and high-efficacy enclosing solution,whilst the non-orthogonal property between axial and tangential speed components,non-ignorable environmental perturbations,and strict assignment requirements empower elliptical encircling control to be more challenging,and the relevant investigations are still open.Following this line,an appointed-time elliptical encircling control rule capable of reinforcing circumnavigation performances is developed to enable Unmanned Aerial Vehicles(UAVs)to move along a specified elliptical path within a predetermined reaching time.The remarkable merits of the designed strategy are that the relative distance controlling error can be guaranteed to evolve within specified regions with a designer-specified convergence behavior.Meanwhile,wind perturbations can be online counteracted based on an unknown system dynamics estimator(USDE)with only one regulating parameter and high computational efficiency.Lyapunov tool demonstrates that all involved error variables are ultimately limited,and simulations are implemented to confirm the usability of the suggested control algorithm.

Distributed Multicircular Circumnavigation Control for UAVs with Desired Angular Spacing

This paper addresses a multicircular circumnavigation control for UAVs with desired angular spacing around a nonstationary target.By defining a coordinated error relative to neighboring angular spacing,under the premise that target information is perfectly accessible by all nodes,a centralized circular enclosing control strategy is derived for multiple UAVs connected by an undirected graph to allow for formation behaviors concerning the moving target.Besides,to avoid the requirement of target’s states being accessible for each UAV,fixed-time distributed observers are introduced to acquire the state estimates in a fixed-time sense,and the upper boundary of settling time can be determined offline irrespective of initial properties,greatly releasing the burdensome communication traffic.Then,with the aid of fixed-time distributed observers,a distributed circular circumnavigation controller is derived to force all UAVs to collaboratively evolve along the preset circles while keeping a desired angular spacing.It is inferred from Lyapunov stability that all errors are demonstrated to be convergent.Simulations are offered to verify the utility of proposed protocol.

Multi-UAVs Collaborative Path Planning in the Cramped Environment

Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles(UAVs)in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search(ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes.The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.

基于排队论的UAVS通信网切换方案分析

针对UAVS通信网切换管理中的信道分配问题,提出了保护切换信道与切换呼叫按照先入先出队列排队相结合的方案(GCM-FIFO),利用Markov链对该方案进行了建模,通过数学推导分析了系统的性能,并就等待队列容量对切换性能的影响进行了讨论和仿真.结果表明:通过实施GCM-FIFO方案,UAVS通信网获得了较好的切换性能,而且随着队列容量的增加,切换呼叫失败的概率呈下降趋势,但是当队列容量超过某个值后,切换呼叫失败的概率趋于恒定,因而应根据系统相关参数,利用仿真结果选择合适的队列容量.

MUAVs系统的混合组织模型

分析多UAV系统的协作控制以及2种基本的组织模型及其优缺点,结合大规模UAVs(MUAVs)系统协作的复杂特征,提出适用于MUAVs系统的几种分级混合网络组织模型以及具有自适应能力的动态杂类混合组织模型。研究结果表明,该方法可有效降低MUAVs系统管理和通信复杂度,提高系统的可靠性与鲁棒性。

随机网格回归Monte Carlo双UAVs最优目标协调跟踪

针对多个无人机(unmanned aerial vehicle,UAV)执行基于视觉的目标跟踪的最佳协调问题,提高不可预知的地面目标的最佳结合点的视觉测量效果,提出了一种基于随机网格回归Monte Carlo的UAV最优目标跟踪策略。首先,通过无人机动力学和目标动力学分析,获得双UAV情况下的随机最优协调控制目标;其次,针对提出的控制目标,引入Monte Carlo求解方案,同时为解决标准Monte Carlo方案中存在的状态空间维度较高,计算复杂且精度不高的问题,利用随机网格方式构建回归Monte Carlo方案,实现UAV的最优协调控制;最后,通过仿真实验验证了所提方法的有效性。

复杂多约束UAVs协同目标分配的一种统一建模方法

多无人机协同目标分配(Multi-UAVs cooperative target assignment problem,MUCTAP)是一个条件复杂的多模型、多约束组合优化问题,很难用一致的方法获得可行的解.为解决该问题,本文对各种目标分配模型统一建模,设计了一致的模型处理方法;针对三维环境特点,提出利用空间垂直切面计算无人机估计航程代价的方法,并利用航程代价矩阵优化目标分配算法;同时加入协同约束关系计算,提高分配算法的准确性.仿真实验验证了该方法能够处理多种情况的目标分配问题,具有较高的通用性和准确性,能够有效完成多机协同目标分配任务.

UAVS通信网切换管理方案设计与分析

针对UAVS通信网中的切换管理问题,提出了融合滞后余量与门限结合的切换判决方法、基于UAV节点自主决策的紧急切换控制策略和GCM-FIFO信道分配方案的切换管理方案。相关理论分析表明,该切换管理方案解决了UAVS通信网中节点高移动性、切换业务量高但信道数有限以及由于中继机节点遭遇威胁而带来的紧急切换等问题。

A Software-in-the-Loop Implementation of Adaptive Formation Control for Fixed-Wing UAVs

This paper discusses the design and software-in-theloop implementation of adaptive formation controllers for fixedwing unmanned aerial vehicles(UAVs) with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission(e.g.depending on the payload). Modeling and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduP ilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-inthe-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.

Multiple UAVs cooperative formation forming control based on back-stepping-like approach

To ensure multiple unmanned aerial vehicles （UAVs）reach stable formation quickly, a cooperative guidance law basedon the back-stepping-like approach is designed in this paper.Adopting the guidance mechanism of virtue leader vehicle, thedynamic equation of tracking errors for each UAV is built. Thecommunication interactive relationships are described based ongraph theory, and the guidance law for formation reaching is ob-tained by the back-stepping-like approach. The formation stabilityis analyzed by constructing an appropriate Lyapunov function. Thesimulation results have shown that this guidance and control lawcan make each UAV converge to the trajectory of the virtue leaderultimately, and has the quicker rate of convergence and lowertracking error.

Optimal search for moving targets with sensing capabilities using multiple UAVs

This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.

OFDM-UAV中继广播通信系统航迹优化方法

为了提高无人机中继广播通信系统的性能,针对频率选择性衰落信道,考虑基站到无人机以及无人机到用户的全通信链路,提出了基于正交频分复用(OFDM)技术的无人机中继广播通信系统航迹优化方法。首先提出了基于OFDM的无人机中继广播通信传输模型,对单用户节点中断概率和系统平均中断概率进行理论分析并给出近似计算公式;然后以平均中断概率最小化和最大用户中断概率最小化为优化准则,提出无人机的航迹优化方法并分析了各因素对系统中断性能的影响;最后通过计算机仿真,证明了所提航迹优化方法正确有效。研究表明:在多径信道下,基于OFDM的无人机中继广播通信系统能有效克服频率选择性衰落,且译码转发方式下的连通性优于放大转发方式;基于平均中断概率最小化准则得到的系统中断性能优于最大用户中断概率最小化准则。

Cooperative localization against GPS signal loss in multiple UAVs flight

Based on multiple unmanned aerial vehicles（UAVs） flight at a constant altitude,a fault-tolerant cooperative localization algorithm against global positioning system（GPS） signal loss due to GPS receiver malfunction is proposed.Contrast to the traditional means with single UAV,the proposed method is based on the use of inter-UAV relative range measurements against GPS signal loss and more suitable for the small-size and low-cost UAV applications.Firstly,for re-localizing an UAV with a malfunction in its GPS receiver,an algorithm which makes use of any other three healthy UAVs in the cooperative flight as the reference points for re-localization is proposed.Secondly,by using the relative ranges from the faulty UAV to the other three UAVs,its horizontal location can be determined after the GPS signal is lost.In order to improve an accuracy of the localization,a Kalman filter is further exploited to provide the estimated location of the UAV with the GPS signal loss.The Kalman filter calculates the variance of observations in terms of horizontal dilution of positioning（HDOP） automatically.Then,during each discrete computing time step,the best reference points are selected adaptively by minimizing the HDOP.Finally,two simulation examples in Matlab/Simulink environment with five UAVs in cooperative flight are shown to evaluate the effectiveness of the proposed method.

Coalition Formation for Multiple UAVs Cooperative Search and Attack with Communication Constraints in Unknown Environment

A coalition formation algorithm is presented with limited communication ranges and delays in unknown environment,for the performance of multiple heterogeneous unmanned aerial vehicles(UAVs)in cooperative search and attack missions.The mathematic model of coalition formation is built on basis of the minimum attacking time and the minimum coalition size with satisfying resources and simultaneous strikes requirements.A communication protocol based on maximum number of hops is developed to determine the potential coalition members in dynamic network.A multistage sub-optimal coalition formation algorithm(MSOCFA)with polynomial time is established.The performances of MSOCFA and particle swarm optimization(PSO)algorithms are compared in terms of complexity,mission performance and computational time.A complex scenario is deployed to illustrate how the coalitions are formed and validate the feasibility of the MSOCFA.The effect of communication constraints(hop delay and max-hops)on mission performance is studied.The results show that it is beneficial to determine potential coalition members in a wide and deep range over the network in the presence of less delay.However,when the delays are significant,it is more advantageous to determine coalitions from among the immediate neighbors.

Development of Bionic UAVs Cluster Technology

With the wider use of UAV in various fields,the raising task complexity and the increasing environmental uncertainties,the higher requirements for UAV technology are put forward.The bionic UAV cluster system has the potential advantages of good stealth performance,strong environmental adaptability,wide expansion of task and high operational efficiency.It has excellent prospects in future information warfare,electronic warfare and conventional combat field.By reviewing the development of bionic UAV cluster technology,this paper summarizes and analyzes the latest research progresses of the key technologies such as aerodynamic mechanism and aerodynamic configuration design,driving mechanism design,autonomous flight control,adaptive networking and cluster control of bionic aircrafts.

基于多密度流聚类的UAV-NOMA用户分簇与功率分配算法

针对无人机(Unmanned Aerial Vehicle,UAV)辅助非正交多址(Non-Orthogonal Multiple Access,NOMA)下行通信系统,提出了最大化和速率的用户动态分簇与功率分配方案.考虑用户服务质量与UAV位置约束,建立了和速率最大化的优化问题.由于目标函数的非凸性,将原问题解耦为三个子问题,分别优化UAV位置部署与用户连接、用户动态分簇、功率分配以提高系统性能.首先,基于K-means算法设计了UAV位置部署与用户连接方案,以减小路损为目的确定UAV最佳部署位置,同时选择其服务的最优用户群;其次,改进多密度流聚类(Multi-Density Stream Clustering, MDSC)算法,提出了单UAV下用户静态与动态分簇方案,静态分簇方案可自适应平衡簇数与簇用户数,并获得较大的簇内用户信道增益差异,动态分簇方案则针对用户移动属性,制定了即时更新策略;最后,使用分式规划(Fractional Programming,FP)二次变换的方法,引入辅助变量将原非凸问题变换为凸问题,交替优化辅助变量与功率分配因子,获得原非凸问题的次优解.仿真结果表明,与其他算法相比,本文分簇方案能获得更大的簇内信道差异与更小的簇内用户数标准差,同时用户系统性能也获得了显著提升.

An adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs

To overcome nonlinear and 6-DOF(degrees of freedom)under-actuated problems for the attitude and position of quadrotor UAVs,an adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs is proposed,in which an adaptive law is designed to online estimate the parameter variations and the upper bound of external disturbances and the assessments is utilized to compensate the backstepping sliding mode control.In addition,the tracking error of the design method is shown to asymptotically converge to zero by using Lyapunov theory.Finally,based on the numerical simulation of quadrotor UAVs using the setting parameters,the results show that the proposed control approach can stabilize the attitude and has hover flight capabilities under the parameter perturbations and external disturbances.

Robust Tracking Control of Uncertain MIMO Nonlinear Systems with Application to UAVs

In this paper, we consider the robust adaptive tracking control of uncertain multi-input and multi-output (MIMO) nonlinear systems with input saturation and unknown external disturbance. The nonlinear disturbance observer (NDO) is employed to tackle the system uncertainty as well as the external disturbance. To handle the input saturation, an auxiliary system is constructed as a saturation compensator. By using the backstepping technique and the dynamic surface method, a robust adaptive tracking control scheme is developed. The closed-loop system is proved to be uniformly ultimately bounded thorough Lyapunov stability analysis. Simulation results with application to an unmanned aerial vehicle (UAV) demonstrate the effectiveness of the proposed robust control scheme. © 2014 Chinese Association of Automation.

The use of UAVs for landslide disaster risk research and disaster risk management: a literature review

On a global scale,from 2005 to 2019,there were 275 high-magnitude,low-frequency disasters that involved 14,172 fatalities and four million affected people.Similar patterns have taken place during longer periods of time in recent decades.This paper aims to analyse the contribution of the international landslide research community to disaster risk reduction and disaster risk management in reference to the use of Unmanned Aerial Vehicles(UAVs)in a literature review.The first section notes the relevance of disaster risk research contributions for the implementation of initiatives and strategies concerning disaster risk management.The second section highlights background information and current applications of drones in the field of hazards and risk.The methodology,which included a systematic peer review of journals in the ISI Web of Science and SCOPUS,was presented in the third section,where the results include analyses of the considered data.This study concludes that most current scholarly efforts remain rooted in hazards and post-disaster evaluation and response.Future landslide disaster risk research should be transdisciplinary in order to strengthen participation of the various relevant stakeholders in contributing to integrated disaster risk management at local,subnational,national,regional and global levels.

Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.