Dynamic output feedback fault tolerant controller design for discrete-time switched systems with actuator fault

This paper investigates the problem of dynamic output feedback fault tolerant controller design for discrete-time switched systems with actuator fault. By using reduced-order observer method and switched Lyapunov function technique, a fault estimation algorithm is achieved for the discrete-time switched system with actuator fault. Then based on the obtained online fault estimation information, a switched dynamic output feedback fault tolerant controller is employed to compensate for the effect of faults by stabilizing the closed-loop systems. Finally, an example is proposed to illustrate the obtained results.     

Design of an active fault tolerant control system for a simulated industrial steam turbine

An active fault tolerant control (FTC) scheme is proposed in this paper to accommodate for an industrial steam turbine faults based on integration of a data-driven fault detection and diagnosis (FDD) module and an adaptive generalized predictive control (GPC) approach. The FDD module uses a fusion-based methodology to incorporate a multi-attribute feature via a support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) classifiers. In the GPC formulation, an adaptive configuration of its internal model has been devised to capture the faulty model for the set of internal steam turbine faults. To handle the most challenging faults, however, the GPC control configuration is modified via its weighting factors to demand for satisfactory control recovery with less vigorous control actions. The proposed FTC scheme is hence able to systematically maintain early FDD with efficient fault accommodation against faults jeopardizing the steam turbine availability. Extensive simulation tests are conducted to explore the effectiveness of the proposed FTC performances in response to different categories of steam turbine fault scenarios.     

Research on the vehicle routing problem with interval demands

In this paper, a vehicle routing problem with interval demands is investigated based on the motivation of dispatching vehicles to deliver perishable products in practice. A nonlinear interval-based programming method is used to build a model for the vehicle routing problem with interval demands, which assumes that demands of customers are uncertain but fall in given intervals and actual demand of a customer becomes known only when the vehicle visited the customer. A vehicle-coordinated strategy was designed to solve the service failure problem. A hybrid algorithm based on the artificial immune system is also proposed to solve the model for vehicle routing problem with interval demands. The validity of methods and sensitivity analysis are illustrated by conducting some numerical examples. We find that the tolerant possibility degree of interval number has significant impacts on the distances. The planned distance strictly increased, while the additional distance strictly decreased and the total distance after coordinated transport has a U-typed relationship with the tolerant possibility degree of interval number.     

Incrementally extensible hypercube networks and their fault tolerance

The incrementally extensible hypercube or IEH graph, a generalization of the hypercube, was introduced in [1]. It is shown that IEH graphs are incrementally extensible in steps of 1, optimally fault tolerant and its diameter is logarithmic in the number of nodes. Moreover, for any given number of nodes, the difference of the maximum and the minimum degree of a node in the graph is ≤ 1; i.e., the graph is almost regular. We also analyze these graphs in presence of node failures. We develop a fault tolerant routing algorithm which can compute a minimal path between two arbitrary nodes in an IEH graph in the presence of the maximum number of allowable faults. We also determine the fault diameter of the graph. Thus, we show that the IEH graphs, while they can be defined for any integer number of computing nodes, retain all the desirable properties of regular hypercubes including fault tolerance and a tight fault diameter.     

Fault Tolerant Sorting—Theoretical and Empirical Analyses of the Randomized QuickMergesort Algorithm

This paper introduces a new paradigm in the design of sorting algorithms, viz., fault tolerance. Fault tolerance is an important concept in modern day computing and design workflows must accommodate this need. In general, there are a number of avenues for faults to occur and techniques to address the same; this paper focusses on only one source of faulty behavior, viz., process termination. Process termination, as a cause of faulty behavior, is important from the perspective of various applications in real-time scheduling. In order to measure the effectiveness of a fault tolerant protocol, it is necessary to define a suitable metric and analyze the performance of the protocol with respect to that metric. We measure the “unsortedness” of an array, as characterized by the number of inversion pairs that remain when the sorting algorithm (process) terminates. This paper proposes a new algorithm for sorting called the Randomized QuickMergesort (RQMS) algorithm. RQMS has a higher degree of fault tolerance than either Randomized Quicksort (RQS) or Mergesort (MS), in that fewer inversion pairs remain when it terminates. Likewise, RQMS has a lower comparison overhead than RQS and is more space-efficient than MS. Our empirical analysis, which was conducted over a wide variety of distributions, conclusively establishes that RQMS is the algorithm of choice, when fault tolerance is paramount in the application. This research was supported in part by the Air Force Office of Scientific Research under Contract FA9550-06-1-0050.     

基于故障树和混沌粒子群算法的锻压机床故障诊断方法

锻压机床由于生产效率高和材料利用率高的特点,被广泛应用于各领域.然而,锻压机床发生故障时,其故障种类繁多、故障数据量大,所以对锻压机床故障源的快速、准确诊断较困难.针对该问题,文章提出一种将故障树分析法和混沌粒子群算法相融合的方法,对锻压机床的故障源进行故障诊断.该方法是先通过故障树分析法对锻压机床的故障进行分析从而得到故障模式及其故障概率,然后由得到的故障模式和已知的故障维修经验分析归纳出故障模式的学习样本,再根据得到的故障概率运用混沌粒子群算法的遍历性快速、准确地诊断出锻压机床发生故障的精确位置.文章提出的方法以锻压机床的伺服系统为例进行了故障诊断实验,将该实验结果与遗传算法、粒子群算法进行对比.实验结果表明,文章的算法在锻压机床伺服系统的故障诊断中准确度更高、速度更快.     

Special algorithms for the simulation of homogeneous random fields

In this paper, two new algorithms for the simulation of homogeneous random fields are proposed. Both algorithms are based on the well-known algorithm of rows and columns for the simulation of Gaussian fields with special correlation functions. The algorithms make it possible to efficiently simulate homogeneous random fields with nonconvex correlation functions.     

基于分子动力学模拟的金属构件的弹-塑性分解方法

针对金属多晶材料构件的分子动力学（MD）模拟,本文提出了一种新的弹-塑性分解方法.文章将MD运动轨迹分解为结构变形和热振动,给出了计算结构变形的方法和近似公式;针对金属多晶材料构件的当前构型,给出了基于FCC|BCC晶胞和四原子占位的四面体单元相组合的连续变形函数及计算变形梯度的算法;利用原子-连续关联模型,发展了计算当前构型应力场和弹性张量的算法.分析了当构件承受过大载荷后在材料内部所产生的微观缺陷,并将其分类标定为位错、层错、挛晶界、晶界和空位等;对于层错和挛晶界讨论了在弹性卸载过程中应满足的刚体运动约束方程;利用极小势能原理构造了基于当前构型的弹性卸载算法,进而给出了完整的基于MD模拟的计算弹-塑性应变的算法.最后,针对单晶铜纳米线拉伸的MD模拟,计算了弹-塑性应变场,验证了本文方法的合理性.
本文提出的基于MD模拟的弹-塑性分解方法,为从微观到宏观的多尺度和多模型耦合计算提供了算法支撑.     

随机的库存-路径问题的机会约束规划模型与算法

随机需求下的库存-路径问题是一类复杂的组合优化问题.本文讨论了VMI背景下的库存-路径联合优化问题,构建了问题的机会约束规划模型,并将随机模拟、人工神经网络和遗传算法结合在一起,设计了求解问题的混合智能算法.实验表明算法性能良好.     

Integration of AI and OR Techniques for Computer-Aided Algorithmic Design in the Vehicle Routing Domain

This paper shows how tools and techniques of artificial intelligence can be successfully integrated into a computer system working in the vehicle routing domain. The aim of this system, called ALTO, is to facilitate the development of routing algorithms for transportation vehicles. In this paper, we describe the general algorithmic framework and the rich interface provided by the system to the expert algorithm designer. We also introduce a methodology for acquiring useful knowledge in the domain, based on examples of successful and unsuccessful problem-solving strategies. With such knowledge, ALTO would then be capable of actively supporting the algorithm designer by suggesting good candidate algorithms for solving new problems.     

连续代数扩域上多项式因式分解的Trager算法

多项式的因式分解是符号计算中最基本的算法,二十世纪六十年代开始出现的关于多项式因式分解的工作被认为是符号计算领域的起源．目前多项式的因式分解已经成熟,并已在Maple等符号计算软件中实现,但代数扩域上的因式分解算法还有待进一步改进．代数扩域上的基本算法是Trager算法．Weinberger等提出了基于Hensel提升的算法．这些算法是在单个扩域上做因式分解．而在吴零点分解定理中,多个代数扩域上的因式分解是非常基本的一步,主要用于不可约升列的计算．为了解决这一问题,吴文俊,胡森、王东明分别提出了基于方程求解的多个扩域上的因式分解算法．王东明、林东岱提出了另外一个算法Trager算法相似,将问题化为有理数域上的分解．他们应用了吴的三角化算法,因此算法的终止性依赖于吴方法的计算．支丽红则将提升技巧用于多个扩域上的因式分解算法．本文将Trager的算法直接推广为连续扩域上的因式分解,只涉及结式计算与有理数域上的因式分解,给出了多个代数扩域上的因式分解一个直接的算法．     

基于交通流的多模糊时间窗车辆路径优化

研究了基于交通流的多模糊时间窗车辆路径问题,考虑了实际中不断变化的交通流以及客户具有多个模糊时间窗的情况,以最小化配送总成本和最大化客户满意度为目标,构建基于交通流的多模糊时间窗车辆路径模型。根据伊藤算法的基本原理,设计了求解该模型的改进伊藤算法,结合仿真算例进行了模拟计算,并与蚁群算法的计算结果进行了对比分析,结果表明,利用改进伊藤算法求解基于交通流的多模糊时间窗车辆路径问题,迭代次数小,效率更高,能够在较短的时间内收敛到全局最优解,可以有效的求解多模糊时间窗车辆路径问题。     

一种基于双链量子编码的动态车辆路径问题解决策略

针对配送调度事件动态变化的动态车辆路径问题(DVRP), 以最小化运输成本、最小化配送时间 与最大化载货率为目标, 建立了问题的数学模型,提出了改进的多相量子粒子群算法. 针对DVRP问题的特点,提出基于车辆链和货物链的双链量子编码方法; 同时设计了基于周期和 重调度因子驱动的动态调度策略. 最后将方法应用于动态仿真算例, 并与其他经典算法比较, 结果验证了所提出方法的有效性.     

Qualitative fault tolerance analysis for a class of hybrid systems

This paper investigates the fault tolerance problem of hybrid systems with faults that affect the switching sequence. A hierarchical model is developed, which consists of four parts from bottom to top: hybrid automata, qualitative abstraction, discrete abstraction, and supervisor. Under this model, the reachability and fault tolerability properties of hybrid systems can be analyzed in a qualitative manner and from discrete-event system point of view. Sufficient conditions are given to maintain the system’s ideal (or acceptable) performance in spite of faults. Several numerical examples and an application to hose insertion task illustrate the efficiency of the proposed method.     

Stabilization of active fault‐tolerant control systems by uncertain nonhomogeneous markovian jump models

This article investigates the stabilization and control problems for a general active fault‐tolerant control system (AFTCS) in a stochastic framework. The novelty of the research lies in utilizing uncertain nonhomogeneous Markovian structures to take account for the imperfect fault detection and diagnosis (FDD) algorithms of the AFTCS. The underlying AFTCS is supposed to be modeled by two random processes of Markov type; one characterizing the system fault process and the other describing the FDD process. It is assumed that the FDD algorithm is imperfect and provides inaccurate Markovian parameters for the FDD process. Specifically, it provides uncertain transition rates (TRs); the TRs that lie in an interval without any particular structures. This framework is more consistent with real‐world applications to accommodate different types of faults. It is more general than the previously developed AFTCSs because of eliminating the need for an accurate estimation of the fault process. To solve the stabilizability and the controller design problems of this AFTCS, the whole system is viewed as an uncertain nonhomogeneous Markovian jump linear system (NHMJLS) with time‐varying and uncertain specifications. Based on the multiple and stochastic Lyapunov function for the NHMJLS, first a sufficient condition is obtained to analyze the system stabilizability and then, the controller gains are synthesized. Unlike the previous fault‐tolerant controllers, the proposed robust controller only needs to access the FDD process, besides it is easily obtainable through the existing optimization techniques. It is successfully tested on a practical inverted pendulum controlled by a fault‐prone DC motor. © 2016 Wiley Periodicals, Inc. Complexity 21: 318–329, 2016     

Detection,isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

The goal in many fault detection and isolation schemes is to increase the isolation and identification speed. This paper, presents a new approach of a nonlinear model based adaptive observer method, for detection, isolation and identification of actuator and sensor faults. Firstly, we will design a new method for the actuator fault problem where, after the fault detection and before the fault isolation, we will try to estimate the output of the instrument. The method is based on the formation of nonlinear observer banks where each bank isolates each actuator fault. Secondly, for the sensor problem we will reformulate the system by introducing a new state variable, so that an augmented system can be constructed to treat sensor faults as actuator faults. A method based on the design of an adaptive observers’ bank will be used for the fault treatment. These approaches use the system model and the outputs of the adaptive observers to generate residues. Residuals are defined in such way to isolate the faulty instrument after detecting the fault occurrence. The advantages of these methods are that we can treat not only single actuator and sensor faults but also multiple faults, more over the isolation time has been decreased. In this study, we consider that only abrupt faults in the system can occur. The validity of the methods will be tested firstly in simulation by using a nonlinear model of waste water treatment process with and without measurement noise and secondly with the same nonlinear model but by using this time real data.     

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilisations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing based strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature.     

双需求集货送货一体化车辆路径问题的数学模型及算法

本文研究了具有总时间和车容量约束的双需求集货送货一体化车辆路径问题,在综合考虑运输费用和车辆出行固定费用的前提下,建立了该问题的整数线性规划模型,并分别给出了求解该模型的精确算法和基于节约准则的启发式算法。最后通过一个具体实例验证了算法的有效性。     

概率因果模型结合改进遗传算法的核动力系统故障分析

将概率因果模型的似然函数作为遗传算法的适应函数,从而将复杂系统的故障诊断转化为最优问题,建立了一种将概率因果模型和遗传算法相结合的故障诊断方法,并利用改进算法与概率因果故障诊断模型对船用核动力装置进行故障诊断实例分析,分析结果对船用核动力装置故障诊断具有重要的指导意义,而改进遗传算法是进行船用核动力装置故障诊断有效而实用的方法。     

基于Voronoi划分的同城即时配送优化策略研究

近年来经济社会发展及新零售业强势崛起使得平台或商家对大规模即时配送需求日益增加,在求解大规模车辆路径问题时仅使用启发式算法或其融合算法已无法满足实际需求。本文针对基于分众级的同城即时配送模式及现阶段存在的问题,确定了基于Voronoi划分算法的即时配送分区方法和对基础蚁群算法的三个改进策略;并以全程配送产生的总成本最少为目标函数,构建了带用户需求软时间窗的车辆路径问题数学模型;最后选取客户、车辆以及门店共计一百二十个真实地理位置数据,验证了本文提出的求解策略的有效性,并分析最终结果。结果显示,①使用Voronoi分区-改进蚁群算法的两阶段方法求解大规模车辆路径问题能显著减少配送总成本,同时提升客户满意度;②在多门店的条件假设下,采用改进蚁群算法求解得到的超时时间比基础蚁群算法少36%,配送总成本低17%。