伺服机构故障下基于线性规划的运载火箭姿控系统重构控制

发动机伺服机构故障给新一代运载火箭姿控系统的可靠性和安全性带来挑战,亟须开展重构控制策略研究。针对这一问题,提出一种基于线性规划的摆角重构控制分配方法。将伺服机构故障下的摆角分配问题转化为1范数单目标有约束优化问题,进而转化为标准的线性规划模型,采用单纯形法进行求解。仿真结果表明,所提出的线性规划法能够实现伺服机构故障下姿控系统的完全重构,各摆角均未达到饱和值,表明了方法的有效性。     

Non‐zero‐sum nonlinear network path interdiction with an application to inspection in terror networks

A simultaneous non‐zero‐sum game is modeled to extend the classical network interdiction problem. In this model, an interdictor (e.g., an enforcement agent) decides how much of an inspection resource to spend along each arc in the network to capture a smuggler. The smuggler (randomly) selects a commodity to smuggle—a source and destination pair of nodes, and also a corresponding path for traveling between the given pair of nodes. This model is motivated by a terrorist organization that can mobilize its human, financial, or weapon resources to carry out an attack at one of several potential target destinations. The probability of evading each of the network arcs nonlinearly decreases in the amount of resource that the interdictor spends on its inspection. We show that under reasonable assumptions with respect to the evasion probability functions, (approximate) Nash equilibria of this game can be determined in polynomial time; depending on whether the evasion functions are exponential or general logarithmically‐convex functions, exact Nash equilibria or approximate Nash equilibria, respectively, are computed. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 139–153, 2017     

遗传算法及其在导弹火力分配上的应用

遗传算法是一种近年来新发展起来的优化算法,目前它已被广泛应用于解决许多实际问题,如函数优化、图像识别、机器学习、人工神经网络、人工生命、优化调度等许多领域。通过对一种遗传算法的研究,阐述其在导弹武器火力分配上的应用。首先设计和实现了一种遗传算法;然后描述了导弹火力分配优化问题,建立了其数学模型;最后运用遗传算法求解了该问题。     

坦克分队在进攻战斗中的最佳火力分配

运用兰切斯特方程和极值理论就坦克分队在进攻战斗中如何分配火力及使火力分配最佳进行了研究。给出了最佳方案的计算方法     

Bi‐objective missile rescheduling for a naval task group with dynamic disruptions

This paper considers the rescheduling of surface‐to‐air missiles (SAMs) for a naval task group (TG), where a set of SAMs have already been scheduled to intercept a set of anti‐ship missiles (ASMs). In missile defense, the initial engagement schedule is developed according to the initial state of the defensive and attacking units. However, unforeseen events may arise during the engagement, creating a dynamic environment to be handled, and making the initial schedule infeasible or inefficient. In this study, the initial engagement schedule of a TG is assumed to be disrupted by the occurrence of a destroyed ASM, the breakdown of a SAM system, or an incoming new target ASM. To produce an updated schedule, a new biobjective mathematical model is formulated that maximizes the no‐leaker probability value for the TG and minimizes the total deviation from the initial schedule. With the problem shown to be NP‐hard, some special cases are presented that can be solved in polynomial time. We solve small size problems by the augmented ? ‐ constraint method and propose heuristic procedures to generate a set of nondominated solutions for larger problems. The results are presented for different size problems and the total effectiveness of the model is evaluated.     

SpaceWire网络热点通信模式的缓存资源分配算法

基于SpaceWire网络在热点通信模式下的工作特点,研究缓存资源的分配算法。给出均匀通信模式和热点通信模式的释义;推导网络路由节点的满负荷概率和平均延时的解析方法,计算网络中的关键通信节点;再给出SpaceWire网络缓存资源分配算法。利用Opnet网络仿真平台建立SpaceWire通信模型,仿真了不同通信模式中采用缓存优化策略前后的关键性能指标。研究结果表明:在热点通信模式状态下,该缓存资源分配算法在保证总资源一定的前提下,网络系统的平均延时降低,优化了SpaceWire网络性能。     

Mixed rules in multi‐issue allocation situations

Multi‐issue allocation situations study problems where an estate must be divided among a group of agents. The claim of each agent is a vector specifying the amount claimed by each agent on each issue. We present a two‐stage rule. First, we divide the estate among the issues following the constrained equal awards rule. Second, the amount assigned to each issue is divided among the agents in proportion to their demands on this issue. We apply the rule to two real‐world problems: the distribution of natural resources between countries and the distribution of budget for education and research between universities.     

A specially structured nonlinear integer resource allocation problem

We present an algorithm for solving a specially structured nonlinear integer resource allocation problem. This problem was motivated by a capacity planning study done at a large Health Maintenance Organization in Texas. Specifically, we focus on a class of nonlinear resource allocation problems that involve the minimization of a convex function over one general convex constraint, a set of block diagonal convex constraints, and bounds on the integer variables. The continuous variable problem is also considered. The continuous problem is solved by taking advantage of the structure of the Karush‐Kuhn‐Tucker (KKT) conditions. This method for solving the continuous problem is then incorporated in a branch and bound algorithm to solve the integer problem. Various reoptimization results, multiplier bounding results, and heuristics are used to improve the efficiency of the algorithms. We show how the algorithms can be extended to obtain a globally optimal solution to the nonconvex version of the problem. We further show that the methods can be applied to problems in production planning and financial optimization. Extensive computational testing of the algorithms is reported for a variety of applications on continuous problems with up to 1,000,000 variables and integer problems with up to 1000 variables. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 770–792, 2003.     

多操纵面战斗机飞行控制系统设计

现代多操纵面战斗机飞行控制系统多采用非线性控制方法进行设计,其中逆系统方法具有物理概念清晰、参数对应明确、无不确定性算法、容易结合传统经验等优点,易于工程实现.将该方法应用于多操纵面战斗机的基本控制律设计,使用多层简化设计,改进了多操纵面的控制分配策略,并针对某型多操纵面战斗机六自由度模型进行数字仿真,效果良好.     

不确定情况下协同空战目标分配方法

现代空战环境下出现的大量不确定信息, 现在常用的协同目标分配模型都没有考虑到这种情况.针对这种情况,将随机规划引入到协同空战目标分配研究中,在一般期望值模型的基础上,建立了多机空战协同目标分配期望值模型,并利用随机模拟、神经网络和遗传算法结合而成的混合智能算法来求解该期望值模型.2∶4协同空战仿真结果表明该模型的有效性.