Circular Formation Flight Control for Unmanned Aerial Vehicles With Directed Network and External Disturbance

This paper proposes a new distributed formation flight protocol for unmanned aerial vehicles(UAVs)to perform coordinated circular tracking around a set of circles on a target sphere.Different from the previous results limited in bidirectional networks and disturbance-free motions,this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound.Distinguishing from the design of a common Lyapunov fiunction for bidirectional cases,we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as input.Then the whole control system is regarded as a cascade connection of these two subsystems,which is proved to be stable by input-tostate stability(ISS)theory.For the purpose of encountering the external disturbance,the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere(say,the circular tracking control algorithm)with the help of the switching function.Meanwhile,the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere(say,the formation keeping control law)for formation keeping.The validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.     

Unmanned Aerial Vehicles: Control Methods and Future Challenges

With the rapid development of computer technology,automatic control technology and communication technology,research on unmanned aerial vehicles(UAVs)has attracted extensive attention from all over the world during the last decades.Particularly due to the demand of various civil applications,the conceptual design of UAV and autonomous flight control technology have been promoted and developed mutually.This paper is devoted to providing a brief review of the UAV control issues,including motion equations,various classical and advanced control approaches.The basic ideas,applicable conditions,advantages and disadvantages of these control approaches are illustrated and discussed.Some challenging topics and future research directions are raised.     

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Establishing a system for measuring plant health and bacterial infection is critical in agriculture. Previously, the farmers themselves, who observed them with their eyes and relied on their experience in analysis, which could have been incorrect. Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light, both visible and eye using a drone. The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles (UAVs) with an ensemble classification technique. Convolution neural networks in unmanned aerial vehicles image were used. To convey this interest, the rice’s health and bacterial infection inside the photo were detected. The project entailed using pictures to identify bacterial illnesses in rice. The shape and distinct characteristics of each infection were observed. Rice symptoms were defined using machine learning and image processing techniques. Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria. The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.     

四旋翼无人机自适应导航控制

研究四旋翼(Quadrotor)无人机导航控制问题。针对传统的四旋翼无人机导航控制方法的目标定位误差和实时性差问题,提出了基于CLOS技术的导航控制方法。采用CLOS技术所开发的导航控制系统使得四旋翼无人机能够在移动停机坪完成自主导航和着陆的任务,并详细研究了导航控制系统的设计和仿真。仿真结果显示了所设计的导航控制系统的性能和有效性,可应用于四旋翼无人机的实时导航。     

基于三维程控的无人机协同编队飞行研究

基于三维程控飞行策略,对多无人机(UAV)协同编队飞行控制进行研究,提出一种多机同方位任务需求的编队控制方法.采用“长机-僚机”编队结构模式控制编队飞行,以编队中的长机航迹坐标为基准坐标系实现了僚机与长机相对位置一致的控制;实现了编队中所有无人机同时到达指定位置并保持速度相对稳定的控制.通过航路规划和编队遥调,实现了人工干预与自动控制结合的编队飞行策略.仿真结果表明,该方法具有较好的可实施性、管理性、应用性和安全性.     

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles (UAVs) or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance, due to their ability to perform repetitive and tedious tasks in hazardous environments. Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional (3D) flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature. However, not a single optimization algorithms can solve all kind of optimization problem effectively. Therefore, there is dire need to integrate metaheuristic for general acceptability. To address this issue, in this paper, a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm (QGA) has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then, utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment. The performance of the developed QGA has been compared against the various metaheuristics. The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.     

基于XML的无人机任务链模型

对无人机系统地面任务控制站与飞行器通信协议中的任务链进行了研究。根据功能将任务链分为航路计划、传感器计划、通信计划和控制权限交接计划4个部分，建立了能够支持复杂任务的任务链模型。采用XML语言对任务链进行表达，增强了任务链的标准性和通用性。最后实现了任务控制站通过任务链控制多架无人机协同执行任务的仿真。     

基于模型预测控制的无人机时空协同航迹规划

为了求解同时实现空间协同和时间协同的多无人机时空协同问题,提出了基于分布式模型预测控制的多无人机在线协同航迹规划的方法。建立了由MPC（Model Predictive Control,）控制器、空间协同模块和时间协同模块组成的多无人机分布式时空协同航迹规划框架结构。MPC将时空协同问题转化为滚动优化问题,优先级的方法实现了空间协同和时间协同的解耦,同时改进了碰撞冲突消解规则,并设计了时间冲突消解规则,解决了分布式时空协同问题的动作一致性问题。仿真实验表明,该方法可以有效地实现多无人时空协同航迹规划。     

混合动力汽车模型预测控制策略研究

针对传统混合动力汽车控制方法无法实现实时最优控制的问题,提出了基于简化混合动力汽车系统模型的预测控制智能优化策略.通过将3自由度的系统模型简化为1自由度的系统模型,并采用连续广义最小残量方法求解模型预测控制问题.运用MATLAB/Simulink与GT-POWER联合仿真平台进行仿真,实验结果验证了系统模型简化的有效性,以及所设计的模型预测控制算法大幅度提高混合动力汽车的燃油经济性的能力和实时控制性能.     

Temporal Range Registration for Unmanned Ground and Aerial Vehicles

An iterative temporal registration algorithm is presented in this article for registering 3D range images obtained from unmanned ground and aerial vehicles traversing unstructured environments. We are primarily motivated by the development of 3D registration algorithms to overcome both the unavailability and unreliability of Global Positioning System (GPS) within required accuracy bounds for Unmanned Ground Vehicle (UGV) navigation. After suitable modifications to the well-known Iterative Closest Point (ICP) algorithm, the modified algorithm is shown to be robust to outliers and false matches during the registration of successive range images obtained from a scanning LAser Detection And Ranging (LADAR) rangefinder on the UGV. Towards registering LADAR images from the UGV with those from an Unmanned Aerial Vehicle (UAV) that flies over the terrain being traversed, we then propose a hybrid registration approach. In this approach to air to ground registration to estimate and update the position of the UGV, we register range data from two LADARs by combining a feature-based method with the aforementioned modified ICP algorithm. Registration of range data guarantees an estimate of the vehicle's position even when only one of the vehicles has GPS information. Temporal range registration enables position information to be continually maintained even when both vehicles can no longer maintain GPS contact. We present results of the registration algorithm in rugged terrain and urban environments using real field data acquired from two different LADARs on the UGV. ★Commercial equipment and materials are identified in this article in order to adequately specify certain procedures. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.     

On Trajectory Tracking Model Predictive Control of an Unmanned Quadrotor Helicopter Subject to Aerodynamic Disturbances

In this article a model predictive control (MPC) strategy for the trajectory tracking of an unmanned quadrotor is presented. The quadrotor's dynamics are modeled using a hybrid systems approach and, specifically, a set of piecewise affine (PWA) systems around different operating points of the translational and rotational motions. The proposed control scheme is dual and consists of an integral MPC for the translational motions, followed by an MPC scheme for the tracking of the quadrotor's attitude motions. By the utilization of PWA representations, the controller is computed for a larger part of the quadrotor's flight envelope, which provides more control authority for aggressive maneuvering. The proposed dual control scheme is able to calculate optimal control actions with robustness against atmospheric disturbances (e.g. wind gusts) and with respect to the physical constraints of the quadrotor (e.g. maximum lifting forces or fixed thrust limitations in order to extend flight endurance). Extended simulation studies indicate the efficiency of the MPC scheme, both in trajectory tracking and aerodynamic disturbance attenuation.     

基于神经网络的无人机滑模飞行控制设计

针对无人机受扰运动,基于Backstepping方法和非线性滑模控制提出了一种鲁棒神经网络飞行控制方案。对无人机姿态角速度层的系统不确定性项,采用径向基函数神经网络并对其权值进行在线调整,从而实现对其进行逼近。将回馈递推设计方法与滑模控制方法结合起来,基于神经网络的输出为无人机设计了一种回馈递推滑模飞行控制器。所设计的飞行控制器用于无人机的姿态控制,仿真结果表明所研究的无人机鲁棒神经网络飞行控制方案是有效的。     

A Flocking-Based on Demand Routing Protocol for Unmanned Aerial Vehicles

研究人员的某社区显示出到自治雄蜂或 UAV (无人的天线车辆) 的兴趣与无线通讯网络的来临增加了。这些网络允许 UAV 以一种特定的方式更高效地合作以便在特定的环境完成特定的任务。到那么,当保持经由收音机连接在它的组与另外的节点连接了时,每只雄蜂独立地遨游。这个连接能故意被维持一会儿抑制雄蜂的活动性。这将对涉及在来源和一个目的地之间的给定的传播的一条给定的路径的雄蜂合适。这限制能在传播过程的结束被移开,每只担心的雄蜂的活动性从其它变得再独立。在这个工作,我们建议为 UAV 的一个基于成群的路由协议叫了 BR-AODV。协议为当数据正在被播送时,维持的连接和线路为雷纳兹机制的按需的线路计算,和 Boids 利用一个众所周知的特定的路由协议。而且,自动扎根的基础车站发现机制为为即时应用程序的上下文需要的一个积极雄蜂和地面网络协会被介绍了。BR-AODV 的表演被评估,与古典 AODV 路由的相比,协议和结果证明 BR-AODV 以延期,产量和包损失超过 AODV。     

基于状态空间MPC的无人机无人车联合运动控制

针对四旋翼无人机无人车联合运动缺乏对系统成员姿态约束的问题,研究了一种基于模型预测控制(MPC)的分布式联合运动控制方法.基于虚拟结构法,使用虚拟领航者策略,以虚拟领航者提供参考轨迹及参考速度,分别在各无人器平台上转换成各自所需的预测时域信息,结合推导的各无人器的状态空间模型滚动优化实现预测控制.限定四旋翼高度方向运动状态与偏航角,构造以俯仰角、横滚角与重力加速度乘积为位置运动输入的状态空间模型,将无人机内环姿态控制约束加入位置运动,增强飞行稳定性.改良无人车状态空间模型,增加速度信息得到可提供位置速度追踪的增广状态空间模型,增强运动追踪能力.仿真表明在满足无人器姿态约束条件下,能够保证联合运动的位置速度精度.     

Nonlinear Adaptive Close Formation Control of Unmanned Aerial Vehicles

This paper treats the question of formationflight control of multiple unmanned aerial vehicles (UAVs). Inclose formation the wing UAV motion is affected by the vortexof the adjacent lead aircraft. The forces produced by these vorticesare complex functions of the relative position coordinates ofthe UAVs. In this paper, these forces are treated as unknownfunctions. For simplicity, it is assumed that the UAVs have autopilotsfor heading-, altitude-, and Mach-hold in the inner loops. Anadaptive control law is derived for the position control of thewing aircraft based on a backstepping design technique. In theclosed-loop system, commanded separation trajectories are asymptoticallytracked by each wing aircraft while the lead UAV is maneuvering.It is seen that an overparametrization in the design is essentialfor the decentralization of the control system. These resultsare applied to formation flight control of two UAVs and simulationresults are obtained. These results show that the wing UAV followsprecisely the reference separation trajectories in spite of theuncertainties in the aerodynamic coefficients, while the leadaircraft maneuvers.     

3自由度旋翼飞行机械臂系统动力学建模与预测控制方法

旋翼飞行机械臂(rotorcraft aerial manipulator,RAM)系统是安装在飞行机器人上的可操作型机械臂,悬停模式下执行准确的空中操作时旋翼无人机与所加机械臂之间存在相对扰动,通过分离机械臂与飞行机器人进行动力学建模并不能有效消除这种扰动.本文基于对相互扰动力学作用的分析建立整体动力学模型,并在悬停飞行模式下将其简化为线性控制参考模型.进而对旋翼系统控制延时所引起的动力学扰动进行补偿,同时设计预测控制器来消除末端执行器的位置和姿态误差.最后,在存在内部和外部扰动的情况下,设定销钉插入操作任务进行控制方法的对比仿真.末端执行器位姿偏差的仿真结果表明了模型结构与控制方法的有效性.     

插电式混合动力汽车模型预测控制策略研究

针对传统插电式混合动力汽车智能控制策略计算量大,难以实现实时最优控制的问题,提出了基于蓄电池充放电管理的插电式混合动力汽车预测控制策略.利用实测通勤插电式混合动力汽车车速信息,以蓄电池荷电状态为系统状态变量,以蓄电池充放电功率为系统控制变量,插电式混合动力汽车燃油消耗量最低为系统性能指标,设计了插电式混合动力汽车的模型预测控制智能优化算法,运用连续广义最小残量方法求解最优控制问题.在Matlab/Simulink与GT-POWER联合仿真平台上进行仿真,实验结果验证了所设计的模型预测控制算法不仅可以大幅度提高混合动力汽车的燃油经济性,而且能够满足实时控制的要求.     

基于多任务的无人机编队控制研究

考虑到多架无人机编队飞行的特点,将松散编队及协同思想应用到紧密编队控制中,提出了一个三架无人机协同作战编队的飞行控制系统设计方法;在编队飞行动力学模型的基础上,设计了基于特征结构配置的无人机横侧向控制律,进行指定航路的飞行控制;然后,设计编队控制器,两架僚机可紧紧跟随长机并保持队形稳定;仿真结果表明,设计的控制器可以控制多架无人机进行紧密编队飞行,具有一定的实用性和推广价值。     

具有未知干扰的无人机鲁棒滑模飞行控制

研究无人机飞行稳定性控制问题,由于无人机飞行控制系统存在时变外部干扰,飞行过程中升阴比变化激烈,控制稳定性难度较大。利用滑模控制良好的鲁棒能力提出一种神经网络的鲁棒飞行控制方法。因神经网络有良好非线性逼近能力,可对无人机飞行系统中的不确定进行在线逼近,并将神经网络权值误差引入到权值的自适应律中用以改善系统的动态性能。利用神经网络的组合,设计无人机鲁棒滑模飞行控制器。控制器分为两部分,一部分是等效控制器,另一部分是滑模控制器,能有效减小系统的跟踪误差。最后将所设计的鲁棒滑模控制对无人机飞行姿态控制进行仿真。仿真结果表明,新方法能提高无人机的鲁棒飞行控制能力且能实现无人机姿态的精确跟踪和稳定性控制。     

基于定制内点法的多无人机协同轨迹规划

为提高多无人机(Unmanned aerial vehicles, UAV)协同轨迹规划(Cooperative trajectory planning, CTP)效率, 在解耦序列凸优化(Sequential convex programming, SCP)方法基础上, 提出一种高效求解凸优化子问题的定制内点法. 首先引入松弛变量, 构建子问题的等价描述形式, 并推导该形式下的子问题最优性条件. 然后在预测−校正原对偶内点法的框架下, 构建一套高效求解最优性条件方程组的计算流程以降低子问题计算复杂度, 并利用约束矩阵特征提出一种快速计算原对偶搜索方向的方法以提高规划效率. 仿真结果表明, 在解耦序列凸优化框架下, 定制内点法可将协同轨迹规划耗时降低一个数量级, 达到秒级.