Adaptive fault-tolerant control based on boundary estimation for space robot under joint actuator faults and uncertain parameters

Since the joint actuator of the space robot executes the control instructions frequently in the harsh space environment, it is prone to the partial loss of control effectiveness (PLCE) fault. An adaptive fault-tolerant control algorithm is designed for a space robot system with the uncertain parameters and the PLCE actuator faults. The mathematical model of the system is established based on the Lagrange method, and the PLCE actuator fault is described as an effectiveness factor. The lower bound of the effectiveness factors and the upper bound of the uncertain parameters are estimated by an adaptive strategy, and the estimated value is fed back to the control algorithm. Compared with the traditional fault-tolerant algorithms, the proposed algorithm does not need to predetermine the lower bound of the effectiveness factor, hence it is more in line with the actual engineering application. It is proved that the algorithm can guarantee the stability of the closed-loop system based on the Lyapunov function method. The numerical simulation results show that the proposed algorithm can not only compensate for the uncertain parameters, but also can tolerate the PLCE actuator faults effectively, which verifies the effectiveness and superiority of the control scheme.     

A worst‐case formulation for constrained ranking and selection with input uncertainty

In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.     

Information theory for ranking and selection

We study the classical ranking and selection problem, where the ultimate goal is to find the unknown best alternative in terms of the probability of correct selection or expected opportunity cost. However, this paper adopts an alternative sampling approach to achieve this goal, where sampling decisions are made with the objective of maximizing information about the unknown best alternative, or equivalently, minimizing its Shannon entropy. This adaptive learning is formulated via a Bayesian stochastic dynamic programming problem, by which several properties of the learning problem are presented, including the monotonicity of the optimal value function in an information-seeking setting. Since the state space of the stochastic dynamic program is unbounded in the Gaussian setting, a one-step look-ahead approach is used to develop a policy. The proposed policy seeks to maximize the one-step information gain about the unknown best alternative, and therefore, it is called information gradient (IG). It is also proved that the IG policy is consistent, that is, as the sampling budget grows to infinity, the IG policy finds the true best alternative almost surely. Later, a computationally efficient estimate of the proposed policy, called approximated information gradient (AIG), is introduced and in the numerical experiments its performance is tested against recent benchmarks alongside several sensitivity analyses. Results show that AIG performs competitively against other algorithms from the literature.     

基于深度森林的网络流量分类方法

随着网络应用的迅猛发展,流量分类在网络资源分配、流量调度和网络安全等诸多研究领域受到广泛关注。现有的机器学习流量分类方法对流量数据特征的选取和分布要求苛刻,导致在实际应用中的复杂流量场景下分类精确度和稳定度难以提高。为了解决样本特征属性的复杂性给分类性能带来的不利影响,引入了基于深度森林的流量分类方法。该算法通过级联森林和多粒度扫描机制,能够在样本数量规模和特征属性选取规模有限的情况下,有效地提高流量整体分类性能。通过网络流量公开数据集Moore对支持向量机、随机森林和深度森林机器学习算法进行训练和测试,结果表明基于深度森林的网络流量分类器的分类准确率能够达到96. 36%,性能优于其他机器学习模型。     

军事物流服务商准入标准设置和甄选研究综述

军事物流服务商甄选研究是新军事变革下供应商管理发展的重要方向,是有效完成后勤保障任务、顺利实施作战行动的重要保证。基于此,本文从供应商关系理论和发展、物流服务商指标体系选择以及结合其他方法对物流服务商甄选三个方面对国内外研究现状进行总结概述,对整个军事物流服务商的选择过程加以研究,将其科学客观地运用于军事物流服务商选择的实际操作中,对于加强军事物流服务商需求管理、提高选择质效以及优化工作流程具有重大意义。同时,分析现有研究成果的不足,并对其未来发展前景提出展望,针对性地提出了军事物流服务商初步准入标准以及后续复选的方法,为军事物流服务商选择体系的完善提供参考。     

Fully sequential indifference‐zone selection procedures with variance‐dependent sampling

Fully sequential indifference‐zone selection procedures have been proposed in the simulation literature to select the system with the best mean performance from a group of simulated systems. However, the existing sequential indifference‐zone procedures allocate an equal number of samples to the two systems in comparison even if their variances are drastically different. In this paper we propose new fully sequential indifference‐zone procedures that allocate samples according to the variances. We show that the procedures work better than several existing sequential indifference‐zone procedures when variances of the systems are different. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006     

中庭烟气控制系统性能化设计

介绍了中庭烟气控制性能化设计的发展概况和一般步骤,探讨了中庭烟气控制系统性能化设计方法和参数确定,提出我国实现中庭烟气控制性能化设计有待进一步研究和探讨的问题。     

一种基于生物免疫原理的入侵检测新模型

入侵检测系统可以从生物免疫系统的很多特点中得到启发,文中利用生物免疫原理设计了一个新的入侵检测框架模型,该框架在传统信息传输网基础上构建了免疫淋巴网,用来监控和管理传统传输网的行为。模型中还应用了阴性选择、克隆选择等免疫算法,使得该模型对于入侵检测问题有较好的敌我识别功能。     

基于信息熵的炮兵射击目标选优分析

针对炮兵射击目标选择的特点,运用信息熵权重和层次分析方法,建立射击目标选择的数学模型,得到炮兵射击目标的合理排序。最后得出利用信息熵和层次分析法对射击目标进行选择,比单纯运用层次分析法收敛速度快,又利于计算机软件实现,为炮兵作战指挥自动化系统辅助决策提供了一种新的方法。     

复合战斗部爆炸成型弹丸结构参数影响分析

应用AUTODYN仿真软件,对一种复合战斗部方案的爆炸成型弹丸成型性能进行了分析,结果表明,相对于传统布局方案,破片方案更改后改变了药型罩的成型过程,导致爆炸成型弹丸形状变差从而降低了毁伤性能。通过对结构参数影响进行分析,提出了优化方案,解决了破片方案调整后爆炸成型弹丸的成型问题。