基于遗传算法与DSM的产品结构分解聚类方法

产品结构的分解聚类在产品开发中有着重要的地位,在用设计结构矩阵(D es ign structure m atrix,DSM)对产品结构进行建模的基础上,通过遗传算法实现了产品结构分解聚类的智能化和分解聚类结果的最优化。在算法的设计过程中开发了一种对DSM进行二维编码的方法,并给出了在二维编码基础上的多点杂交和基本变异方法。在构造适应度函数时,综合考虑了DSM模型中各元素之间的联系、聚类的数目以及各聚类中元素的数目。最后以某摩托车发动机为例,用该算法实现了产品结构DSM模型的智能化分解聚类,验证了该算法的可行性。     

考虑状态约束的弹性高超声速飞行器自适应饱和容错控制

针对弹性高超声速飞行器跟踪控制问题进行研究,主要考虑状态约束、输入饱和、执行器故障等约束条件.首先,将弹性模态、外界干扰和模型参数不确定性归结为一类带有未知上界的系统干扰.其次,根据被动容错思想,针对速度和高度子系统分别设计抗饱和自适应容错控制器,通过引入双曲正切函数和新型屏障李雅普诺夫函数分别处理了输入饱和与状态约束...     

Pareto遗传算法在气动外形优化中的应用

Pareto方法作为一种多目标优化方法,能够一次性获得优化问题对应的不同权重分配情况下的所有最优解集。它与遗传算法结合产生的Pareto遗传算法,是求解多目标优化问题的Pareto最优解集合的一种有效手段。本文将两种常用的Pareto遗传算法,MOGA方法和两支联赛遗传算法应用到气动外形优化中,针对具体算例进行气动外形的优化设计,得到了满意的优化设计结果。     

A new target tracking filter based on deep learning

At present, current filters can basically solve the filtering problem in target tracking, but there are still many problems such as too many filtering variants, too many filtering forms, loosely coupled with the target motion model, and so on. To solve the above problems, we carry out crossapplication research of artificial intelligence theory and methods in the field of tracking filters. We firstly analyze the computation graphs of typical a-β and Kalman. Through analysis, it is concluded that ...     

和差通道相位差对差模跟踪接收机的影响分析

在数据中继卫星系统中,中低轨航天器常采用差模跟踪技术,完成对数据中继卫星的捕获跟踪,建立高速数据中继传输链路。文章根据某任务跟踪接收机实际设计参数,用Matlab／Simulink软件对和差通道相位差对跟踪性能的影响进行了仿真分析。     

An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features

In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years, the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper, which is based on the Hyperspectral Features (HFs) of hypersonic targets covered with plasma sheath during high-speed flight. First, a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs. Then, the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar. Finally, an unsupervised classification method based on the Density Peak Clustering (DPC) for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy. The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set. The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient.     

Adaptive vector field based accurate homing control of aerial delivery systems

This paper investigates the homing control problem of a flexible aerial delivery system with external wind, at-mospheric turbulence, and aerodynamic uncertainties. An accurate homing control strategy is presented, consisting of a trajectory generation algorithm and a lateral tracking controller. A high-altitude trajectory generation is de-veloped with system characteristics explicitly considered to generate the desired trajectory for the aerial delivery control system design. It significantly compensates for the altitude deviation of the existing multiphase theory based trajectory generation methodologies. A novel adaptive vector field control law is then designed to accom-plish the lateral tracking maneuvers. The key feature of the proposed method is that the complete lateral closed-loop control, including position and heading angle loops, is achieved in the presence of disturbances and dynamic uncertainties. The homing control with high landing accuracy is therefore achieved. Simulation and hardware-in-loop testing results are finally presented to validate the proposed method’s effectiveness compared to a conventional homing control scheme.     

动态负调约束下固体冲压发动机燃气流量控制算法研究

变流量固体冲压发动机工作过程中，常采用控制燃气发生器压强的方式调节燃气生成量，在跟踪变化压强指令过程中，流量会产生明显的负调现象，严重影响发动机系统的安全性和可靠性。为 解决这一问题，本文以具有高度非线性、强时变特性的燃气发生器系统为对象，构建了基于粒子群算法的流量调节自适应PI控制器，并给出一种利用自适应微分跟踪器对期望控制指令进行“整形”的负调改善算法；首先，结合负调发生、终止判据，综合考量系统流量安全裕度，负调发生方向，构建了适配于动态负调约束的自适应跟踪微分器（TD），克服了传统跟踪微分器在负调抑制过程中控制精度不高，指向性不强等缺陷；进一步，开展了多工况下的负调抑制算法仿真校验，结果表明：所设计自适应控制算法具备良好的燃气流量控制能力，可实现在基本不改变响应时间情况下，将最大负调量削减56.6%的控制效果，展现出良好的工程应用前景。     

Output formation tracking for networked systems with limited energy and aperiodic silence

For leader-following networked systems with the topology switching and the aperiodic silence, limited-energy output formation tracking problems are investigated. Firstly, a new output formation tracking control protocol is proposed, which contains two components associated with the communication interactions between the leader and tracking intelligent agents and the communication interactions among tracking intelligent agents, respectively, and the aperiodic silence, the topology switching and t...