Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

The paper proposes a new swarm intelligence-based distributed Model Predictive Control (MPC) approach for coordination control of multiple Unmanned Aerial Vehicles (UAVs). First, a distributed MPC framework is designed and each member only shares the information with neighbors. The Chaotic Grey Wolf Optimization (CGWO) method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem (FHOCP). Then, the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint. Further, an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach, which considers the predicted state errors and the convergence of cost function. Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.     

Distributed coordinated control scheme of UAV swarm based on heterogeneous roles

This paper proposes a new distributed coordinated control scheme based on heterogeneous roles for Unmanned Aerial Vehicle(UAV) swarm to achieve formation control. First, the framework of the distributed coordinated control scheme is designed on the basis of Distributed Model Predictive Control(DMPC). Then, the effect of heterogeneous roles including leader, coordinator and follower is discussed, and the role-based cost functions are developed to improve the performance of coordinated control for...     

基于反步推演法的多机编队队形重构控制

针对多无人机（UAV）集结期望的队形和达到稳态速度缓慢影响作战效率，基于反步推演法设计了一种协同导引控制律，用于解决多无人机快速队形重构和快速达到稳定状态。本文以一架虚拟长机为中心，3架僚机在3个顶点组成的三角形编队作为被控对象，且长机的速度方向作为编队的前行方向，僚机跟随长机编队飞行。采用长机导引机制，建立每架僚机的误差动力学模型；基于图论建立任意两架无人机之间的通讯模式，通过反步推演法得到多无人机编队队形保持的导引控制律。通过构建合理的Lyapunov函数，证明所提出的控制方法在编队集结和队形保持的有效性，同时将所提出的方法与模型预测控制（MPC）方法和拉普拉斯方法进行对比，更进一步验证所提方法有效性。仿真结果表明：每架无人机不仅能够按照期望的队形飞行，而且以动态响应快和稳态误差小收敛于虚拟长机的运动轨迹。     

基于Leader-Follower的多无人机编队轨迹跟踪设计

针对四旋翼无人机（UAV）群在轨迹跟踪过程中易受外界干扰而引起跟踪误差的问题，设计了基于Leader-Follower的多无人机协同编队轨迹跟踪控制方法。在该系统中，首先通过积分反步法（IBS）对所建四旋翼飞行器模型设计Leader无人机的轨迹跟踪控制器。其次设计了滑模控制（SMC）器，以控制Leader与Follower无人机实现期望的编队队形并同时跟踪参考轨迹。然后通过数值仿真验证了算法的有效性，仿真结果表明，系统具有良好的控制精度。最后通过视觉定位系统进行实验，结果表明所设计的控制器能够实现多个无人机轨迹跟踪和编队控制，所设计的算法具有可行性。     

面向林火持续侦察的多无人机分布式控制方法

为解决目前面向林火持续侦察多无人机（UAV）协同控制实用性与自主性不足的问题，基于蔓延速度诱导元胞自动机（SVICA）林火蔓延算法、无人机与传感器建模，构建了较为真实的三维多无人机火场侦察仿真环境与侦察效能指标，提出了一种面向林火持续侦察的多无人机双层分布式控制架构，在行动层基于强化学习训练的人工神经网络（ANN），实现了有风条件下无人机自主火场环绕与地形跟随功能，在策略层设计通过时域均匀分布算法进行各无人机空速的离散自主调节，最终达到多无人机林火持续侦察时域分布的均匀性与即时性目的。通过一系列数值仿真实验，验证了所提出的无人机分布控制策略在无人机损失和补充突发情况下的自适应性。基于无人机数量与侦察效能指标关系的实验与研究，定义了无人机出动阈值并验证了无人机长时间出动回收策略。最终实验结果表明，针对林火持续侦察任务，所提出的多无人机分布式控制方法具备一定的有效性与实用性。     

基于改进一致性算法的无人机编队控制

编队飞行是指多架无人机保持以一定队形进行飞行的状态,相比于单架飞机执行任务,无人机编队能够增加搜索面积,提高飞机飞行性能,增大完成任务成功率。编队控制是实现编队安全高效完成指定任务的前提。本文以一致性理论为基础,针对无人机运动模型的特点与实际飞行要求,对基本的一致性算法进行改进,提出了改进一致性无人机编队控制算法。首先利用纵向和横侧向解耦的自动驾驶仪模型给出了无人机的三自由度运动方程,根据机动性与飞行性能要求定义了各方向上的加速度、速度与角速度约束。基于一致性理论,将编队控制分为平面与纵向2个方向进行,在状态控制的基础上,利用各状态变量间的几何关系对无人机运动自由度进行转换,加入编队队形信息,设计了编队控制算法。为了使算法生成的指令信号满足约束条件,提出了"最小调整"约束条件处理策略。依据粒子群算法对各无人机的爬升加速度进行优化,以避免机间碰撞。仿真结果表明：提出的编队控制算法具备编队成形与变换功能,能够使无人机编队状态快速收敛到指定值,且保持指定队形,无人机飞行状态满足所有约束条件。     

Distributed bearing-based formation control of unmanned aerial vehicle swarm via global orientation estimation

Most existing formation control approaches for Unmanned Aerial Vehicle(UAV)swarm assume that global position and global coordinate frame are directly available for each agent. To extend the application domain, this paper proposes a distributed bearing-based formation control scheme, without any reliance on global position or global coordinate frame. The interactions among UAVs are described by a directed topology with two-leader structure. To address the issue of unavailable global coordinate fr...     

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.     

基于滑模的多无人机系统协同编队控制

针对具有非完整约束的多无人机系统编队控制问题，提出了一种基于滑模的协同编队控制算法。控制目标是使多无人机系统能够收敛到期望编队，并且能够跟踪上期望的运动轨迹。在领导-跟随结构中，编队的期望运动轨迹由一个动态的虚拟领导者来表示，仅部分跟随者先验已知虚拟领导者信息，并且所有跟随者之间只能局部交互信息。首先，采用分布式状态观测器，使所有跟随者能够在有限时间内估计出虚拟领导者的状态。然后，利用该观测器的估计状态，提出了基于滑模的协同编队控制算法。最后，基于李雅普诺夫稳定性理论证明了多无人机系统的稳定性,并且通过5架无人机的仿真验证了所提算法的有效性。     

Robust leaderless time-varying formation control for unmanned aerial vehicle swarm system with Lipschitz nonlinear dynamics and directed switching topologies

This paper tackles the robust leaderless Time-Varying Formation(TVF) control problem for the Unmanned Aerial Vehicle(UAV) swarm system with Lipschitz nonlinear dynamics, external disturbances and directed switching topologies. In comparison with the previous achievements on formation control problems, the UAV swarm system with Lipschitz nonlinear dynamics can accomplish the pre-designed TVF while tracking a pre-given trajectory which is produced by a virtual leader UAV in the presence of externa...     

自主多无人机的分散化协同控制

协同前提是无人机(UAV)平台间的通信和信息共享,无人机平台之间信息和计算是高度分布的,无人机平台的运动以及通信拓扑的变化,使得集中式协调控制结构很难实现。以最小通信量为基础的分散协同控制具有可扩展性、异构性和动态可重构性等特点,可靠性和鲁棒性较好。针对多无人机平台分散化协同的特点和要求,建立了集中和分散相结合的多无人机平台协同控制系统结构,集中式任务管理系统主要完成目标分配、通信管理和编队管理功能,分散式协同部分主要实现局部任务规划、协调策略及协调控制等功能。分别以多机协同目标跟踪、多机和多编队一致性协调、多机协同编队控制与重构等多无人机平台分散化协同控制技术为应用对象,探讨了分散化协调机制、策略、控制及其与信息之间关系。给出了部分算法的仿真结果。     

无人机用燃料电池阴极供气系统建模与控制

燃料电池因其高效、无污染、噪声小等特点,被认为是未来最具有潜力的无人机（UAV）用动力源,燃料电池阴极供气系统的控制技术是决定燃料电池系统性能和可靠性的关键。针对无人机用质子交换膜燃料电池（PEMFC）阴极供气系统,首先,考虑外界温度、压力、空气密度以及雷诺数等随高度变化的参数,建立了跨高度离心空压机模型并分析了其在不同高度下的工作特性,基于无刷直流电机反电势特征构建了高速空压机驱动电机模型。其次,通过计算燃料电池阴极氧气和氮气的动态分压获取了PEMFC电堆输出电压。设计了基于分数阶PI^λD^μ的过氧比和阴极气压控制方法,驱动电机采用有限集模型预测控制（MPC）实现快速的转矩响应,仿真结果表明设计的控制器可在无人机跨高度运行条件下实现过氧比的快速调节,同时维持阴极气压稳定,满足燃料电池阴极供气需求。     

集群无人机队形重构及虚拟仿真验证

队形重构是集群无人机（UAV）控制的重要问题，指无人机按照要求安全、无碰撞地从一个队形变换到另一个队形，其难点在于快速规划最优安全轨迹并控制无人机进行轨迹姿态的高精度跟踪。针对集群无人机队形重构的上述问题，首先，基于CAPT（Concurrent Assignment and Planning of Trajectories）算法，解决了多无人机的目标分配和轨迹生成的实时性问题，实现了集群无人机的最优安全路径规划；其次，提出一种有限时间多变量积分滑模连续控制算法，解决了无人机轨迹姿态的高精度跟踪问题，并通过MATLAB仿真验证了该控制算法的有效性；最后，为了更加真实直观地演示无人机三维仿真效果，建立了基于Gazebo-ROS的无人机仿真平台，实现了12架四旋翼无人机队形重构"建模-仿真-可视化"的一体化仿真演示，验证了上述路径规划算法和轨迹姿态控制算法的有效性。     

切换拓扑下无人机集群系统时变编队控制

针对多无人机(UAV)间通信拓扑可能发生变化的情况,研究了具有二阶积分特性的无人机集群系统的轨迹跟踪与时变编队控制问题。基于一致性方法设计了编队控制器,将编队控制问题转换成闭环系统的稳定性问题,引入了切换拓扑平均驻留时间的概念,并在此基础上利用线性矩阵不等式(LMI)方法,给出了控制器设计步骤。通过构造分段连续Lyapunov函数,证明了切换拓扑下无人机集群系统能够实现对指定轨迹的跟踪并且实现时变编队飞行。以三维空间运动的无人机集群系统为例进行了仿真验证,结果表明本文所提方法能够解决切换拓扑下无人机集群系统的轨迹跟踪与时变编队问题。     

Adaptive formation control of quadrotor unmanned aerial vehicles with bounded control thrust

In this paper, the flight formation control problem of a group of quadrotor unmanned aerial vehicles (UAVs) with parametric uncertainties and external disturbances is studied. Unit-quaternions are used to represent the attitudes of the quadrotor UAVs. Separating the model into a translational subsystem and a rotational subsystem, an intermediary control input is introduced to track a desired velocity and extract desired orientations. Then considering the internal parametric uncertainties and external disturbances of the quadrotor UAVs, the priori-bounded intermediary adaptive control input is designed for velocity tracking and formation keeping, by which the bounded control thrust and the desired orientation can be extracted. Thereafter, an adaptive con-trol torque input is designed for the rotational subsystem to track the desired orientation. With the proposed control scheme, the desired velocity is tracked and a desired formation shape is built up. Global stability of the closed-loop system is proven via Lyapunov-based stability analysis. Numer-ical simulation results are presented to illustrate the effectiveness of the proposed control scheme.     

基于信息一致性的无人机紧密编队集结控制

提出了基于信息一致性的分段式无人机紧密编队集结控制策略,将集结过程分为3步:参考集结点选取和目标集结点分配、形成松散编队以及形成紧密编队。首先,以线切入预定航线的方式计算参考集结点,按照松散编队队形展开生成目标集结点,并利用基于三维距离空间的优化选择算法,将目标集结点快速、准确地分配给每架无人机。然后,使用速度一致性实现向目标集结点定点集结和向松散编队伴航集结,通过非精确的航迹控制快速形成松散编队,提高编队集结的效率。接下来,启动速度、姿态一致性来实现编队最终的精确航迹控制,并逐步压缩编队队形进入紧密编队,避免发生碰撞,完成从松散编队到紧密编队的平稳过渡,同时准确地跟踪预定航线。使用协同修正方法抑制了测量误差、协同误差和通信延迟,提高了紧密编队的稳定性和控制精度。最后,基于MATLAB平台环境对所提三维集结控制策略进行了仿真,验证了其合理性与有效性。     

复杂环境下多无人机轨迹姿态协同控制

考虑多无人机在实际飞行过程中的避障需求，在建立面向控制的四旋翼无人机轨迹姿态模型基础上，为克服传统多无人机为每架无人机规划轨迹的不足，研究了基于多无人机中心点的轨迹设计策略。进一步，考虑避障约束，基于半定规划进行迭代区域扩张完成了多无人机的安全飞行区域及队形设计。在此基础上，基于干扰补偿策略为每架无人机设计了协同控制器，最终确保了多无人机在多障碍环境下的安全飞行。     

Multiple model predictive control of perching maneuver based on guardian maps

Considering the strong nonlinearity of Unmanned Aerial Vehicles(UAVs) resulting from high Angle of Attack(AOA) and fast maneuvering, we present a multi-model predictive control strategy for UAV maneuvering, which has a small amount of online calculation. Firstly, we divide the maneuver envelope of UAV into several sub-regions on the basis of the gap metric theory. A novel algorithm is then developed to determine the ploytopic model for each sub-region.According to this, a Robust Model Predictive...     

非合作目标交会的双层MPC全局轨迹规划控制

针对空间非合作目标近距离视线交会中的全局最优鲁棒轨迹规划与控制问题,提出了基于高斯伪谱方法（GPM）和线性时变模型预测控制（LTVMPC）的双层模型预测控制（MPC）算法。在轨迹规划方面,以视线坐标系下的相对轨道动力学为模型、能量最少和控制精度最优为性能指标构建最优控制问题,利用GPM精度高、收敛速度快的特点将最优控制问题转化为易于求解的全局非线性规划问题,在MPC框架下求解得到全局最优的标称轨迹,克服了传统的MPC不适用于全局大范围非线性规划的缺点;在轨迹跟踪控制方面,考虑预测时域内状态转移矩阵的时变特性,设计了LTVMPC算法对标称轨迹进行追踪,避免了存在不确定性时轨迹的重规划,从而降低在线计算量,保证算法在线自主实施,并且采用滚动优化的策略使算法对不确定性具有鲁棒性。由于规划层和控制层考虑的约束相同,因此规划的轨迹是可控、可达的。数字仿真表明,在燃料消耗和交会时间等方面,提出的方法均显著优于传统的MPC方法,相较于传统的MPC方法,新算法的交会时间减少50%左右,燃料消耗降低30%以上。     

Fractional order adaptive robust formation control of multiple quad-rotor UAVs with parametric uncertainties and wind disturbances

In recent times, multiple Unmanned Aerial Vehicles (UAVs) are being widely utilized in several areas of applications such as agriculture, surveillance, disaster management, search and rescue operations. Degree of robustness of applied control schemes determines how accurate a swarm of UAVs accomplish group tasks. Formation and trajectory tracking controllers are required for the swarm of multiple UAVs. Factors like external environmental effects, parametric uncertainties and wind gusts make the controller design process as a challenging task. This article proposes fractional order formation and trajectory tacking controllers for multiple quad-rotors using Super Twisting Sliding Mode Control (STSMC) technique. To compensate the effects of the disturbances due to parametric uncertainties and wind gusts, Lyapunov function based adaptive controllers are formulated. Moreover, Lyapunov theorem is used to guarantee the stability of the proposed controllers. Three types of controllers, namely fixed gain STSMC and fractional order Adaptive Super Twisting Sliding Mode Control (ASTSMC) methods are tested for the swarm of UAVs by performing the numerical simulations in MATLAB/Simulink environment. From the presented results, it is verified that in presence of wind disturbances and parametric uncertainties, the proposed fractional order ASTSMC technique showed improved robustness as compared to the fixed gain STSMC and integer order ASTSMC.