直升机旋翼阵风响应研究

简要综述了当前直升机旋翼阵风响应的理论研究和试验对比研究的成果及发展方向，事实表明，旋翼的阵风响应是一个比较复杂的动态现象，它是研究旋翼在遭遇到一定形式的阵风后，旋翼由阵风诱导的响应参量的动态变化规律，它不仅影响直升机的驾驶品质，而且影响直升机的疲劳载荷和强度，其研究目的不仅在于进行理论分析和试验对比研究，而且还在于研究阵风减缓的机理和装置，阵风响应的研究对于具有较高动应力与振动水平的无铰旋翼的设     

Mission-driven autonomous perception and fusion based on UAV swarm

Distributed autonomous situational awareness is one of the most important foundation for Unmanned Aerial Vehicle (UAV) swarm to implement various missions. Considering the application environment being usually characterized by strong confrontation, high dynamics, and deep uncertainty, the distributed situational awareness system based on UAV swarm needs to be driven by the mission requirements, while each node in the network can autonomously avoid collisions and perform detection mission through limited resource sharing as well as complementarity of respective advantages. By efficiently solving the problems of self-avoidance, autonomous flocking and splitting, joint estimation and control, etc., perception data from multi-platform multi-source should be extracted and fused reasonably, to generate refined, tailored target information and provide reliable support for decision-making.     

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。     

直升机若干关键技术发展研究

近年来,直升机技术表现出跨代发展特征,以总体设计、旋翼系统、传动系统、涡轴发动机、材料和制造技术、航电技术和飞控技术为代表的各技术领域均有较大突破,部分成果已得到应用.同时,高速旋翼机和无人直升机技术领域的发展也呈现出加速态势,在不久的将来将为直升机带来革命性的变革.     

无人机前开伞式降落过程动力学建模与仿真

文章分析了某型无人机降落伞回收阶段的动力学过程,对各工作阶段的无人机和降落伞的组合体进行了数学建模,使用Matlab软件中的Simulink组件搭建了无人机伞降过程的仿真模型,按照真实飞行和回收工况进行了计算,通过仿真结果与试验数据的比较,验证了模型的正确性.     

Trajectory tracking control of a VTOL unmanned aerial vehicle using offset-free tracking MPC

Designing a stable and robust flight control system for an Unmanned Aerial Vehicle (UAV) is an arduous task. This paper addresses the trajectory tracking control problem of a Ducted Fan UAV (DFUAV) using offset-free Model Predictive Control (MPC) technique in the presence of various uncertainties and external disturbances. The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays, parametric uncertainties, and disturbances. The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed, namely the hover, transition, and cruise mode. The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component. Moreover, different uncertainties such as parametric, time delays in state and input, are introduced in translational and rotational components. From the previous work, the Linear Quadratic Tracker with Integrator (LQTI) is used for comparison to corroborate the performance of the designed controller. Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy. Finally, discussions are provided to demonstrate the effectiveness of the given methodology.     

直升机目标的雷达特性分析

为了研究直升机目标的雷达特性,采用了理论分析、统计分析和性能指标体系分析的方法,对直升机与固定翼飞机、不同直升机之间目标的雷达特性进行了对比分析;建立了直升机的雷达特性统计模型,对统计特性结果进行了分析;采用性能指标体系分析的方法对旋翼调制特性进行了分析,得到了直升机目标的雷达特性影响因素和分布规律。其方法和结果可以作为直升机系统、防空武器系统和直升机靶标的设计参考。     

海上多任务飞机与无人机协同反潜作战研究

海上多任务飞机将成为海军执行海上巡逻侦察和反潜作战的主力航空武器装备,海上多任务飞机与无人机联合编队进行协同反潜作战将是未来航空反潜的发展趋势和重要作战方式之一。介绍P-8A海上多任务飞机主要系统配置和作战能力,分析海上多任务飞机的作战使用以及海上多任务飞机与无人机协同作战的概念和模式,包括协同态势感知和协同攻潜作战,指出了未来海上多任务飞机与无人机协同作战涉及的主要关键技术。     

UAV navigation in high dynamic environments: A deep reinforcement learning approach

Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.     

An intelligent target detection method of UAV swarms based on improved KM algorithm

Complete and efficient detection of unknown targets is the most popular application of UAV swarms. Under most situations, targets have directional characteristics so that they can only be successfully detected within specific angles. In such cases, how to coordinate UAVs and allocate optimal paths for them to efficiently detect all the targets is the primary issue to be solved. In this paper, an intelligent target detection method is proposed for UAV swarms to achieve real-time detection requirements. First, a target-feature-information-based disintegration method is built up to divide the search space into a set of cubes. Theoretically, when the cubes are traversed, all the targets can be detected. Then, a Kuhn-Munkres (KM)-algorithm-based path planning method is proposed for UAVs to traverse the cubes. Finally, to further improve search efficiency, a 3D real-time probability map is established over the search space which estimates the possibility of detecting new targets at each point. This map is adopted to modify the weights in KM algorithm, thereby optimizing the UAVs’ paths during the search process. Simulation results show that with the proposed method, all targets, with detection angle limitations, can be found by UAVs. Moreover, by implementing the 3D probability map, the search efficiency is improved by 23.4%–78.1%.