计及诱速和风切变的直升机自转前飞特性

研究直升机自转前飞时在风切变条件下的稳定性和操纵性。风切变采用非线性模型，旋翼模型考虑桨叶的刚性挥舞运动，桨叶根部具有水平铰外伸量和弹性约束，诱速在桨盘处的分布采用固定涡系所表示的诱速非均匀分布模型，以Ｚ９飞机为算例，算出计及风切变与否的自转前飞平衡参数和稳定根及操纵响应，显示了风切变对自转前飞操稳特性的影响。     

翼身融合无人机外形优化设计研究

翼身融合布局无人机具有较好的升阻特性和隐身特性,拥有广泛的应用前景。为了提高无人机的续航能力,兼顾翼身融合无人机的气动特性和结构重量要求,选择无人机升阻比与全机面积作为优化目标,应用多目标优化方法研究翼身融合无人机的外形设计,提出一种针对翼身融合无人机的外形参数化优化设计方法,并进行实例验证。结果表明:外形优化可以提高无人机升阻比、减轻结构重量,从而获得合理的翼身融合无人机设计方案。     

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.     

无人航空器系统适航与地面碰撞模型

无人航空器系统在军事、民用领域快速发展,但适航理论研究发展缓慢,这带来了公众生命、财产安全方面的风险,也限制了无人航空器系统行业的发展。对无人航空器的适航理论及体系进行了分析和研究,引入舆情因子和区域因子,提出一种改进的无人航空器系统适航地面碰撞模型。该模型能够综合反映安全、舆论以及区域特殊性对无人航空器系统适航的影响,可以为其适航管理提供依据,也能作为无人航空器系统行业适航申请的参照。该模型的提出有助于加快无人航空器系统适航研究基础理论的发展。     

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     

固定翼无人机编队集结控制算法研究

针对固定翼无人机协同作战时的编队集结问题，提出了一种新的路径规划和位置分配方法，并设计了包括航迹跟踪、高度保持和速度控制在内的自动驾驶仪。该路径规划算法通过矩阵迭代得到一组较优的目标点分配方案，满足总航程较小和同时到达约束。根据得到的各无人机飞向目标点的航迹，算出无人机编队集结的代价矩阵。在每架无人机确定了应飞航路后，开始沿航路飞向目标点，在此过程中，纵向采用高度保持自动驾驶仪，横向采用航迹跟踪自动驾驶仪，控制无人机按规定航迹飞行。速度调节自动驾驶仪可根据速度指令调节油门大小加减速，跟踪上目标速度，进而实现编队集结。仿真结果验证了所提出的编队集结控制方法的有效性和可行性。     

An efficient aerodynamic shape optimization of blended wing body UAV using multi-fidelity models

This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D(high-fidelity) and 2D(lowfidelity) models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing(AFSQP) is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body(BWB) Unmanned Aerial Vehicle(UAV). The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.     

Adaptive Control of Helicopter Ground Resonance with Magnetorheological Damper

The methodology for adaptive control of helicopter ground resonance with magnetorheological （MR） damper is presented. The adaptive inverse control method is used to control the output damping force of MR damper and the range of the damping force is given. Through the adaptive inverse control, the damping force of MR damper is fit to a desired damping force. With the background of applying MR damper to control of helicopter ground resonance, a model of loss force and an adaptive arithmetic for stabilization of the coupled rotor/fuselage system are presented. The simulation shows that the controller presented in this paper can stabilize the rotor/fuselage coupling system quickly and control the helicopter ground resonance effectively.     

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。