考虑观测的双冲量最优交会研究

针对跟踪星主动交会目标星的轨道转移问题,给出了考虑观测的双冲量最优交会迭代算法。首先对考虑观测的轨道交会问题进行了分析,给出了椭圆转移轨道的存在性判定方法。在此基础上,给出了基于粒子群的优化求解方案。经过对两异面椭圆轨道转移问题的仿真解算,求得了最优交会轨道,验证了算法的正确性。该研究成果可作为工程应用的有益参考,并为其他带约束的轨道交会优化问题提供可行的求解方案。     

基于Nash-Pareto策略的两种改进算法及其应用

针对多目标、多设计变量的优化问题,提出了两种优化的新算法:一种是将多目标问题转化为单目标时,对目标权重的确定提出了新的途径;另一种是直接对多目标问题进行优化,并对Pareto遗传优化技术作了改进,以得到均匀分布的Pareto最优解集.两种新算法都是建立在Nash的系统分解与Pareto遗传算法的基础上,因此称这类算法为Nash-Pareto策略.借助于这类算法,文中以跨声速压气机双圆弧类叶型的气动优化为例,给出了气动优化的全过程.数值优化的实验表明所给出的改进算法是可行的、有效的.     

小推力深空探测轨道全局优化设计

 针对小推力深空探测四维轨道优化设计,给出了一种组合优化算法,采用该算法基于二体模型进行了深空探测四维轨道全局优化。该组合优化算法由动态规划算法、静态参数优化算法与共轭梯度算法组成。动态规划算法和静态参数优化算法用以选择最优的发射窗口、返回窗口及相应的近似飞行轨道;基于该近似轨道方案,采用共轭梯度算法（解决两点边值问题）求解精确的最优轨道。通过大量的数值仿真计算,得到了航天器的全局最优飞行轨道,及相应的最优发射窗口与返回窗口。数值仿真结果表明,该组合优化算法对深空探测轨道优化具有良好的通用性和工程运用价值。     

一种考虑一阶惯性环节的最优制导律

本文利用微分对策理论方法研究了在三维空间里,以一阶惯性环节考虑导弹和目标动力学因素的最优制导律问题。给出了一个矢量形式的最优反馈制导律,并在反馈增益中引用了系统控制刚度参量k,进而给出了新的综合意义。 最后,在一定条件下,通过进一步近似还给出了一个次优制导律。     

基于启发式蚁群算法的协同多目标攻击空战决策研究

协同多目标攻击空战决策是现代战机在超视距条件下进行协同空战的关键技术之一。它是寻求一个优化分配方案,将目标分配给各友机,力求使攻击效果最优。本文在对协同多目标攻击战术进行深入分析的基础上,提出了一种用于空战决策的启发式蚁群算法,该算法通过求解友机导弹对目标的最优分配来确定空战决策方案。仿真实验表明所提出的启发式蚁群算法对最优解的搜索效率明显优于基本蚁群算法,是一种求解协同多目标攻击空战决策问题的有效算法。     

变循环发动机双涵道模式下变几何控制探索

为了实现变循环发动机的性能优化,利用混合优化算法寻找最优的几何控制变量来实现变循环发动机的性能提升。在节流优化过程中,采用了单独减小高压转子转速和协同调节高低压转子转速两种控制方案,并计算了巡航状态时在这两种控制方案下发动机由最大推力到50%最大推力的节流过程中,发动机性能参数和工作参数的变化。计算结果表明:在高度为11km、马赫数为0.9条件下,保持进口流量不变,在50%最大推力时协同调节方案相比单一调节方案,发动机的耗油率下降了5.997%,另一个巡航状态(高度为9km,马赫数为0.8)下也有相似的结果。这表明采用高低压转速协同控制能对发动机进行更有效的控制,进一步改善了发动机的巡航性能。     

飞控系统受限控制量线性规划最优分配

概要地介绍了具有冗余操纵面的飞控系统中受限制量分配问题的概念,数学表述方法和直接最优分配方案及其几何算法,并指出该算法在存在难于计算机实现的困难。通过分析,将该问题转化为标准线性规划问题,给出了详细的转化过程,从而使其易于计算机实现。最后通过算例说明了这处改进和算法是正确有效的。     

多段翼型气动力优化设计的多标准高效进化算法

本文分别结合基因算法与博弈论中的竞争型Nash对策及分级型Stackelberg对策构造了多标准高效进化算法,并对二维三段翼型成功地进行了多标准位置增升优化。基因算法在合适地选择了基因操作算子后可以得到全局最优解,博弈论的引入使得传统基因算法具备了无人工干预的多标准优化算法,并且这一优化过程接近于实际翼型工程气动力设计情况。应用本文的方法成功地对三段翼型进行了不同气动条件下的缝翼与襟翼的位置组合优化,最后给出了优化算例,并对两种算法进行了对比。     

基于分解策略的SSO发射轨道遗传全局优化设计

提出了基于轨道分解优化和遗传算法（GA）的SSO发射轨道优化设计策略。针对多个轨道段相互耦合问题,基于分解优化策略,将整个发射轨道设计问题分解为两个轨道段设计问题。为了高效可靠地获得全局最优解,对基本遗传算法进行了改进。首先提出了基于多变异操作等改进措施的改进遗传算法;此外,结合遗传算法的全局搜索特性和Powell算法的局部搜索特性,设计了一种串行混合遗传算法。一个二级SSO运载火箭的计算结果表明,轨道分解优化策略确保了问题的成功求解,改进遗传算法和混合遗传算法均可稳定地获得全局最优解,但是混合算法更有效地提高了GA性能。     

混合遗传算法在最优Lambert轨道转移设计中的应用

研究了初始位置、目标位置和转移时间均不固定的双冲量Lambert转移问题,旨在实现燃料和转移时间的综合最优.在对Lambert飞行时间定理推导的基础上,提出了一种遗传算法和迭代法相结合的优化算法,并给出了算法的设计步骤;同时,对影响遗传算法寻优性能的两个参数进行了自适应调整,提高了算法的收敛性.最后,通过两个实例验证了所提算法的有效性和准确性.     

Closed-form solution for the Kalman filter gains of time-varyingsystems

A method for derivation of closed-form solutions for the differential Riccati matrix equation for specific time-varying systems is presented. It allows more insight into the nature of the solution. It reduces the on-line computation requirements, since it does not require on-line solution of a differential equation. Sufficient conditions for the existence of the closed-form solution are given. The method is applied to a target tracking problem     

基于混合优化的运载器大气层内上升段轨迹快速规划方法

针对运载器大气层内的最优轨迹快速规划问题,提出一种将求解最优控制问题的间接法与直接法相结合的混合优化方法。首先,基于最优控制问题的一阶必要条件,将运载器大气层内的三维最优上升问题转化为Hamiltonian两点边值问题;然后,采用直接法中能以较少的节点获得较高求解精度的Gauss伪谱法进行求解,提高算法的求解效率;最后,采用真空解析解初值及密度同伦技术,解决初值猜测与算法收敛困难的问题。仿真结果表明,混合优化算法能够准确、快速地对运载器大气层内的最优上升轨迹问题进行求解,并在计算精度与效率上均优于间接法,可应用于运载器的轨迹在线规划与闭环制导。     

Distributed decision fusion using empirical estimation

The problem of optimal data fusion in multiple detection systems is studied in the case where training examples are available, but no a priori information is available about the probability distributions of errors committed by the individual detectors. Earlier solutions to this problem require some knowledge of the error distributions of the detectors, for example, either in a parametric form or in a closed analytical form. Here we show that, given a sufficiently large training sample, an optimal fusion rule can be implemented with an arbitrary level of confidence. We first consider the classical cases of Bayesian rule and Neyman-Pearson test for a system of independent detectors. Then we show a general result that any test function with a suitable Lipschitz property can be implemented with arbitrary precision, based on a training sample whose size is a function of the Lipschitz constant, number of parameters, and empirical measures. The general case subsumes the cases of nonindependent and correlated detectors.     

Blind adaptive decision fusion for distributed detection

We consider the problem of decision fusion in a distributed detection system. In this system, each detector makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center. The objective of the fusion center is to optimally fuse the local decisions in order to minimize the final error probability. To implement such an optimal fusion center, the performance parameters of each detector (i.e., its probabilities of false alarm and missed detection) as well as the a priori probabilities of the hypotheses must be known. However, in practical applications these statistics may be unknown or may vary with time. We develop a recursive algorithm that approximates these unknown values on-line. We then use these approximations to adapt the fusion center. Our algorithm is based on an explicit analytic relation between the unknown probabilities and the joint probabilities of the local decisions. Under the assumption that the local observations are conditionally independent, the estimates given by our algorithm are shown to be asymptotically unbiased and converge to their true values at the rate of O(1/k/sup 1/2/) in the rms error sense, where k is the number of iterations. Simulation results indicate that our algorithm is substantially more reliable than two existing (asymptotically biased) algorithms, and performs at least as well as those algorithms when they work.     

强化专利战略设计,提高企业核心竞争力

强化专利战略设计已成为企业提高核心竞争力,有效遏制竞争对手、实现利润最大化的必然选择.针对企业专利战略中的一些问题,提出了相应的对策和建议.     

特征趋势分区Gauss伪谱法解再入轨迹规划问题

针对强约束下的滑翔再入轨迹规划问题,采用基于特征趋势分区的Gauss伪谱法将连续最优问题转换为多个并行非线性规划问题并对其进行求解。针对传统拟谱方法在处理状态受限时,非平滑最优控制解难以收敛的问题,引入Sobolev空间证明了分区并行的收敛性和一致性。基于Letts准则提出了非连续特征趋势提取方法,通过分析离散点配置导数进行了可变分区的迭代设定,并结合导数变化趋势给出了各分区的动态配点数。利用不同阶次的多项式进行精度逼近,保证了在考虑多项强约束条件下弹道规划的可行解获取。结合状态分区的策略进行离线状态的分解,提高了计算效率。最终针对典型的再入应用进行了轨迹规划应用。仿真结果表明,所提出的方法可有效应对多种约束条件,能够根据飞行器的气动能力规划出可行的轨迹,过程条件满足约束,且可适应气动力20%的拉偏范围。     

On the Performance of Some Sequential Multiple-Decision Procedures

The performance of several sequential procedures for the following multiple-decision problem is investigated. Samples from k random processes (or populations) are available, k at a time (one from each process), to a receiver or data processor. One process contains a signal; the other k - 1 are statistically identical noise. The receiver is to select the odd process (locate the signal), with prescribed probability of error. The optimal receiver makes the selection in minimum average time. Analytical and Monte Carlo computations were performed under the hypothesis that the processes sampled are Rayleigh; however, a method for extrapolating results to other cases is given. The parameter k is allowed to vary from 2 to 1000.     

直接求解变比热斜冲波接体最大尖劈角与最小马赫数

由于按强弱两波角接近的方法收敛精度不高，进一步导出直接求解变比热斜冲波接体最大尖劈角与最小马赫数的公式及解法，并用双精度计算机程序作出例题，得出由最大失劈角可反求出原给定马赫数为最小马赫数的满意结果。同时也给出相应定比热ｋ＝１．４结果的百分误差。     

A quantum inspired genetic algorithm for multimodal optimization of wind disturbance alleviation flight control system

This paper develops a Quantum-inspired Genetic Algorithm (QGA) to find the sets of optimal parameters for the wind disturbance alleviation Flight Control System (FCS). To search the problem domain more evenly and uniformly, the lattice rule based stratification method is used to create new chromosomes. The chromosomes are coded and updated according to quantum-inspired strategies. A niching method is used to ensure every chromosome can converge to its corresponding local minimum in the optimization process. A parallel archive system is adopted to monitor the chromosomes on-line and save all potential feasible solutions in the optimization process. An adaptive search strategy is used to gradually adjust the search domain of each niche to finally approach the local minima. The solutions found by the QGA are compared with some other Multimodal Optimization (MO) algorithms and are tested on the FCS of the Boeing 747 to demonstrate the effectiveness of the proposed algorithm.     

Periodic Job Scheduling in a Distributed Processor System

This investigation considers the problem of the nonpreemptive scheduling of a set of independent periodic jobs in a computer system characterized by a collection of processors, memories, and interconnection mechanisms. Such a collection of resources can be used to perform the functions of a process control environment as are found, for example, in the avionics of a military or commercial aircraft. The distribution of these resources permits weight, volume, power, cost, and reliability requirements to be satisfied. Each periodic job is characterized by its frequency and its execution time. The inverse of the frequency, the period, is assumed to be an integer, and the initiation time and the completion time of job must be strictly met. An optimal algorithm is developed for a job set whose members are related by a binary frequency distribution. That is, if the jobs in the set are ordered in terms of decreasing job execution frequency, then fi = 2fi+1. The optimal result is generalized to include conditions in which the jobs are related by fi = kfi+1, where k is an integer.