基于改进人工蜂群算法的多值属性系统故障诊断策略

针对实际工程应用中常见的多值属性系统故障诊断策略问题,在传统人工蜂群算法(Artificial Bee ColonyAlgorithm,简称 ABC算法)的基础上,提出改进蜂群算法用于多值属性系统的测试序列寻优。首先,在蜂群算法中重新定义多值 D矩阵和五元组的含义;其次,引入方向信息概率矩阵,设置状态转移规则和矩阵元素更新策略;最后,采用导弹舵系统实例说明算法的实现过程和有效性。最终测试序列寻优结果表明:与传统的多值 Rollout算法相比,文中所提算法能得到较好的诊断策略结果,具有一定的应用价值。     

高空长航时飞行器机翼跨声速气动优化设计

高空长航时飞行器应具有升阻比高和俯仰力矩小的气动特性.基于遗传算法的随机性和隐含并行性,结合适用于多目标处理的Pareto方法,在低雷诺数和跨声速条件下,展开了高空长航时飞行器机翼外形的多目标、多设计点的数值优化设计.仿真计算结果表明,所设计的机翼具有较高的升阻比和较低的俯仰力矩,同时在设计马赫数附近升阻比的变化比较平稳,具有良好的综合气动性能.     

小样本条件下航空武器打击精度评价算法

针对现有航空武器打击精度评价算法评价范围比较狭小,评价结果比较粗糙的问题,利用数理统计的理论和方法对航空武器打击精度的评价算法进行了深入研究,提出可变双临界值评价算法.对于给定的精度要求,通过调整可靠度,利用随之而变的上临界值与下临界值两个指标对航空武器打击精度进行准确客观的评价.理论分析和实例计算表明算法是有效可行的.     

俄制引进装备中数字板件国产化研制替代的探讨

针对维修中俄制引进地空导弹武器系统国产化数字板件替代装备联调过程中出现的延时累计误差“瓶颈”问题，分析研究了具体解决办法和措施，并通过了武器系统实弹打靶检验。     

简化七阶CQKF及其在SINS大失准角初始对准中的应用

针对捷联惯性导航系统（SINS）大失准角初始对准的滤波过程，提出简化七阶正交容积卡尔曼滤波算法（7th-SCQKF）。根据简化容积卡尔曼滤波(CKF)理论，推导出改进的简化七阶CKF算法（7th-MSSRCKF），改进了原简化七阶CKF（7th-SSRCKF）的性能；同时，在7th-SSRCKF和7th-MSSRCKF基础上，引入正交半径准则，提出简化七阶CQKF（7th-SCQKF）算法，提高了滤波精度；并将简化七阶CQKF（7th-SCQKF）算法应用到SINS的大失准角初始对准仿真中，结果表明，7th-SCQKF比7th-MSSRCKF的滤波精度高，验证了新算法的正确性和有效性。     

面向下行多天线传输的能效最优上行训练

针对时分双工(TDD,Time Division Duplex)多天线系统的下行传输,研究了以能量效率(EE,Energy Efficiency)最大为准则的上行训练设计问题.证明了存在信道估计误差时系统EE和频谱效率(SE,Spectrum Efficiency)上界是上行训练长度的凹函数,分析了信噪比和电路功耗等因素对两种准则下最优训练长度的影响.分析和仿真结果表明,当信噪比很高时,基于EE最优的设计将退化成基于SE最优的设计；而一般情况下,与最大化SE的训练设计相比,最大化EE要求系统配置更长的训练.     

基于结构模态的机翼带吊舱有限元建模方法

针对无有限元模型的颤振计算建模问题,提出了一种采用结构优化方法建立有限元模型的方法。依据标准算例的结构尺寸和材料特性建立其有限元模型,采用集中质量单元和连接杆模拟外挂吊舱结构,得到了机翼带吊舱的标准模型。计算其结构模态并作为优化目标,建立几何尺寸、质量与标准模型一致。但结构参数不一致的初始模型;然后采用敏度优化算法优化初始模型,得到机翼带吊舱的优化模型。计算结果表明,优化模型与标准模型的颤振速度和频率均较为接近。     

混合回归模型及其在高耸结构风响应时域分析中的应用

针对多维ＡＲ模型同时求解回归系数矩阵所有元素的一些不足之处，本文提出了混合回归模型。通过对某一高耸结构的分析，表明混合回归模型对脉动风的模拟是有效的，并人数值模拟角度验证了，对于线性结构，外激的各态历经性可保证响应的各态历经性。     

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO),the network-wide 4D flight trajectories planning (N4DFTP) problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs) (3D position and time) for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategic level conflict management is developed in this paper.Specifically,a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm (MOMMA) that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the pro posed MOMMA is effective to solve the N4DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.     

Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle (UCAV) performing autonomous air-to-surface (A/S) attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem (RHC-OCP). In particular, an approximate elliptic launch acceptable region (LAR) model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom (2-DOF) receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.