遗传算法在编队卫星群轨道机动中的应用分析

卫星群机动是航天器发展的一个方向.针对编队卫星群的Lambert机动问题,采用Gim-Alfriend矩阵建立了包含中心轨道根数和摄动项的群卫星的相对运动模型,设计了转移轨道上的卫星群队形协同保持的脉冲控制策略.应用遗传算法对编队卫星群轨道机动问题进行了优化,优化指标分别为卫星群协同变轨过程中总燃料消耗最少或燃料均衡分配最小.分析了群机动过程中燃料消耗的影响因素.算例结果表明遗传算法可以很好地应用于编队卫星群机动问题.     

Improved NSGA-Ⅱ Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism

To improve performances of multi-objective optimization algorithms,such as convergence and diversity,a hybridization-encour-aged mechanism is proposed and realized in elitist nondominated sorting genetic algorithm (NSGA-Ⅱ).This mechanism uses the nor-malized distance to evaluate the difference among genes in a population.Three possible modes of crossover operators-"Max Distance","Min-Max Distance",and "Neighboring-Max"-are suggested and analyzed.The mode of "Neighboring-Max",which not only takes advantage of hybridization but also improves the distribution of the population near Pareto optimal front,is chosen and used in NSGA-II on the basis of hybridization-encouraged mechanism (short for HEM-based NSGA-Ⅱ).To prove the HEM-based algorithm,several problems are studied by using standard NSGA-II and the presented method.Different evaluation criteria are also used to judge these algorithms in terms of distribution of solutions,convergence,diversity,and quality of solutions.The numerical results indicate that the application of hybridization-encouraged mechanism could effectively improve the performances of genetic algorithm.Finally,as an example in engineering practices,the presented method is used to design a longitudinal flight control system,which demonstrates the obtainability of a reasonable and correct Pareto front.     

改进遗传算法在飞机总体参数优化中的应用

基于飞机总体参数设计中的多目标优化问题,提出了改进的多目标遗传算法.算法围绕Pareto最优解的概念,利用遗传算法的内在并行性,设法求取多目标优化问题的"Pareto前沿".将不同的改进遗传算法应用于同一干线客机总体参数优化设计中,要求巡航升阻比和有效载荷系数两个目标达到最大,并对各种算法所得的结果进行综合分析与比较,结果显示:基于Pareto排序的多目标优化算法(NSGA,Non-dominated Sorting Genetic Algorithm)的Pareto解最优,可以支配改进的向量评价遗传算法(VEGA,Vector-Evaluated Genetic Algorithm)和随机权重遗传算法(RWGA,Random-Weight Genetic Algorithm)的结果;而VEGA和RWGA的结果互有优劣.      

Geometric Optimization Design System Incorporating Hybrid GRECO-WM Scheme and Genetic Algorithm

This article seeks to outline an integrated and practical geometric optimization design system (GODS) incorporating hybrid graphical electromagnetic computing-wedge modeling (GRECO-WM) scheme and the genetic algorithm (GA) for calculating the radar cross section (RCS) and optimizing the geometric parameters of a large and complex target respectively. A new wedge modeling (WM) scheme is presented for calculating the high-frequency RCS of wedge with only one visible facet based on the method of equivalent currents (MEC). The applications of GODS to 2D cross-section and 3D surface are respectively implemented by choosing an average of monostatic RCS values corresponding to a series of incident angles over a frequency band as the optimum objective function. And the results demonstrate that the RCS can be effectively and conveniently reduced by the GODS presented in this article.     

相干干扰对基于特征空间分解算法性能的影响

分析干扰信号的互相关性和方向矢量正交性对接收信号自相关矩阵的特征值和特征向量分布的影响。干扰相关时,会有信号特征矢量发散到噪声空间,采用基于特征空间分解的自适应算法,只有在相干干扰的来向相同时才能实现干扰抑制,否则不能抑制此相干干扰;干扰不相关时,如果干扰来向相同,只能检测到一个干扰,但此时基于特征空间分解的自适应算法可实现干扰抑制。对MUSIC(multiple signal classification)和最小范数算法的仿真结果,与理论推导一致。     

AUSM系列算法对比研究及背景太阳风初步应用

磁流体力学数值模拟是研究日地物理学现象的一个重要手段. 对比三种AUSM算法, 即AUSM, AUSM+和AUSMPW+, 结合HDC磁场散度消去方法计算多维MHD问题的性能. 通过分析三种算法计算Rotor算例和Orszag-Tang vortex算例的结果发现, AUSM+算法的性能最好. 进一步使用AUSM+算法基于6片网格构造模拟了日冕结构, 计算结果表明这种算法能够正确计算出日冕的大尺度结构. 对于日冕结构模拟块, HDC方法能够较好 地控制磁场散度误差.      

Recent advances in precision measurement & pointing control of spacecraft

There exists an increasing need for precision measurement & pointing control and extreme motion stability for current and future space systems, e.g., Ultra-Performance Spacecraft (UPS). Some notable technologies of realizing Ultra-Pointing (UP) ability have been developed particularly for Ultra-accuracy Ultra-stability Ultra-agility (3U) spacecraft over recent decades. Usually, Multilevel Compound Pointing Control Techniques (MCPCTs) are deployed in aerospace engineering, especially in astronomical observation satellites and Earth observation satellites. Modern controllers and/or algorithms, which are a key factor of MCPCTs for 3U spacecraft, especially the jitter phenomena that commonly exist in a UPS Pointing Control System (PCS), have also been effectively used in some UP spacecraft for a number of years. Micro-vibration suppression approaches, however, are often proposed to deal with low-level mechanical vibration or disturbance in the microgravity environment that is common for UPS. This latter approach potentially is one of the most practical UP techniques for 3U tasks. Some emerging advanced Disturbance-Free Payload (DFP) satellites that exploit the benefits of non-contact actuators have also been reported in the literature. This represents an interesting and highly promising approach for solving some challenging problems in the area. This paper serves as a state-of-the-art review of UP technologies and/or methods which have been developed, mainly over the last decade, specifically for or potentially could be used for 3U spacecraft pointing control. The problems discussed in this paper are of reference significance to UPS and millisecond optical sensors, which are involved in Gaofeng Project, deep space exploration, manned space flight, and gravitational wave detection.     

遗传算法在低信噪比图象点目标检测中的应用

将遗传算法用于图象中低信噪比点目标的检测。在分析了点目标及背景特点的基础上,设计了编码方案及遗传操作算子,提出了合格个体保留法。仿真结果表明应用此算法,可实现信噪比为2dB的点目标的检测与跟踪。     

进化硬件在数字电路设计中的应用研究

进化硬件在航空航天电子系统中有巨大的应用价值和潜力。目前,电路进化设计是进化硬件研究的主要方向之一。本文介绍了进化硬件的原理,并针对平台中算法的早熟和收敛速度慢的缺点,改进了原有算法。同时,指出不同类型数字电路在进化中的区别,并以典型电路为例分别进行进化,对结果进行了比较分析,验证了所用算法对进化不同类型电路的有效性。     

基于遗传算法的正弦波四参数曲线拟合

曲线拟合法能够精确地提取正弦信号的四个参数。但是，由于数据处理比较困难，其应用在一定程度上受到了制约。本文提出了一种基于遗传算法的求解方法，研究了算法应用中初始种群数、繁殖代数和编码方式的选取以及适应度函数的设计。实验数据表明，该方法具有全局收敛和准确度高的优点。