空天体系对抗下未来战斗机发展的一些思考

本文从在未来作战体系中对传统意义上的战斗机有无存在的必要性,未来战斗机的主要作战使命,发展思路,主要特征,使用方式等方面对未来战斗机的特征进行了一些概念性研究和描述。在此基础上,对未来变体战斗机、无人战斗机(UCAV)、未来变体UCAV的关键技术等进行了初步的分析。     

Modeling and simulation of bow wave effect in probe and drogue aerial refueling

In a probe and drogue aerial refueling system, the bow wave of the receiver aircraft will produce a strong aerodynamic effect on the drogue once the receiver follows the drogue at a close distance. It is a major difficulty of docking control in the probe and drogue refueling. This paper analyses the bow wave effect and presents a simple method to model it. Firstly, the inviscid flow around the receiver is modeled based on the stream function defined by basic stream singularities.Secondly, a correction function is developed to eliminate the error caused by the absence of air viscosity. Then, the aerodynamic coefficients are used to calculate the induced aerodynamic force on the drogue. The obtained model is in an analytical form that can be easily applied to the controller design and the real-time simulations. In the verification part, computational fluid dynamics(CFD)simulation tests are conducted to validate the obtained flow fields and aerodynamic forces. Finally,the modeling method is applied to an F-16 receiver aircraft in a previously developed autonomous aerial refueling simulation system. The simulations results are analyzed and compared with the NASA flight-test data, which demonstrates the effectiveness of the proposed method.     

Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function

This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.     

Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory

Unmanned Aerial Vehicle (UAV) swarms have been foreseen to play an important role in military applications in the future, wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults. Therefore, a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms. However, their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system. In this paper, a complex network theory-based robustness evaluation method for a UAV swarming system is proposed. This method takes into account the dynamic evolution of UAV swarms, including dynamic reconfiguration and information correlation. The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies. The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed. Finally, the validity of the proposed method is verified by conducting a case study analysis. The results can further provide some guidance and reference for the robust design, mission planning and decision-making of UAV swarms.     

An aggregate flow based scheduler in multi-task cooperated UAVs network

Unmanned Aerial Vehicles (UAVs) cooperative multi-task system has become the research focus in recent years. However, the existing network frameworks of UAVs are not flexible and efficient enough to deal with the complex multi-task scheduling, because they are not able to perceive the different features. In this paper, a novel cooperated UAVs network framework for multi-task scheduling is proposed. It is a three-layer network including a core layer, an aggregation layer and an execution layer, which enhances the efficiency of multi-task distribution, aggregation and transmission. Furthermore, an AggreGate Flow (AGFlow) based scheduler is dedicatedly designed to maximize the task completion rate, whose key point is to aggregate flows belonging to one task during the multi-task transmission of UAVs network and to allocate priority by calculating the urgency-level of each AGFlow. Simulation results demonstrate that, compared with that of state-of-the-art scheduler, the average task completion rate of AGFlow based scheduler is raised by 0.278.     

Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.     

ATC3000航空地图制作软件及其关键技术分析

针对ATC3000空管自动化系统的运行要求以及飞行管制区现场实际工作的需要,介绍了ATC3000航空地图制作软件的关键技术。文中分析了该软件的结构以及在Windows系统中使用Delphi开发的具体实现技术。     

无人机测控传输中SCCPM优化设计

针对无人机测控传输系统频率资源日趋紧张、电磁环境日渐复杂的现状,为提高无线传输频带效率和功率效率,在卷积码和CPM（Continuous Phase Modulation,连续相位调制）的基础上,提出一种基于SCCPM（Serially Concatenated CPM,串行级联CPM）的无人机遥控、遥测数据传输体制.通过引入Turbo（涡轮）码迭代原理设计CPM解调与卷积译码反馈校正结构,并对CPM解调算法进行优化以减小复杂度,得到相位连续、频谱紧凑、编码增益高、收敛特性好的无人机测控通信系统.仿真结果表明：所设计方案采用级联外迭代方式可获取更高的信噪比增益、更低的错误平层——与典型应用的RS（Reed-Solomon,里德-所罗门）码级联卷积码相比,该方案能够多获取1.6 dB的信噪比增益,表明SCCPM更适用于无人机测控数据传输.     

基于阶层标识的无人机自主精准降落系统

随着微小型无人机（UAV）在航拍、测绘、环境监测、快递投送等民用领域的广泛应用,对微小型无人机的可用性和可靠性提出了更高的要求。为了使微小型无人机能够精确地完成自主降落,由于计算机视觉部署成本低、独立性强、信息丰富等特点,提出了通过识别匹配一种多层嵌套二维编码的阶层降落标识来进行相对定位的算法,并展示了与之对应的阶层标识检测及定位的无人机自主降落系统,由于编码的信息量与其他系统相比,具有高低空的高识别率、编码空间大等特点,故此系统可同时支持单个或多个停机坪的配置,且成本低廉,无需添加机载设备成本。最后对该系统进行仿真验证和实飞测试,表明所提出算法能够有效地实现无人机全自主降落。     

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。