二元相关退化系统可靠性分析及剩余寿命预测

退化相关性和个体差异对二元退化系统可靠性有直接影响,针对该问题,在退化过程模型基础上,建立了相应的系统可靠度和剩余寿命预测模型。首先同时考虑个体退化过程和相关性差异,采用随机参数的Gamma过程和Copula函数建立系统二元相关退化模型,为提高模型适用性,随机参数采用非共轭先验分布假设。在此基础上,分析随机参数对系统可靠度影响,提出基于贝叶斯理论的剩余寿命预测方法。利用马尔可夫链蒙特卡罗(Markov Chain Monte Carlo, MCMC)方法对模型未知参数进行估计。案例分析结果说明了在此类系统可靠性估计时考虑个体差异的必要性,也验证了该剩余寿命预测方法的精确性。     

混杂系统粒子滤波混合状态估计及故障诊断算法

混杂系统同时包含连续动态特性和离散动态特性,并且两种动态相互作用,使其故障诊断变得更加困难。针对此问题,提出了一种混合系统粒子滤波混合状态估计及故障诊断算法,提高了现有方法的适用范围和诊断效率。针对混杂系统受控迁移、自治迁移和随机迁移等特点,首先利用随机混杂自动机对系统离散状态(包括故障)和连续状态进行统一建模,重点对现有基于扩展卡尔曼粒子滤波的连续估计算法进行改进,支持利用在线监测数据来估计混杂系统各类迁移产生的各种离散和连续状态,最后根据离散状态估计结果快速实现故障诊断。通过对典型非线性混杂系统的故障诊断,证明了该方法的有效性。     

二元相关退化系统可靠性分析及剩余寿命预测

退化相关性和个体差异对二元退化系统可靠性有直接影响,针对该问题,在退化过程模型基础上,建立了相应的系统可靠度和剩余寿命预测模型。首先同时考虑个体退化过程和相关性差异,采用随机参数的Gamma过程和Copula函数建立系统二元相关退化模型,为提高模型适用性,随机参数采用非共轭先验分布假设。在此基础上,分析随机参数对系统可靠度影响,提出基于贝叶斯理论的剩余寿命预测方法。利用马尔可夫链蒙特卡罗(Markov Chain Monte Carlo, MCMC)方法对模型未知参数进行估计。案例分析结果说明了在此类系统可靠性估计时考虑个体差异的必要性,也验证了该剩余寿命预测方法的精确性。     

A New Method of State Estimation for Singular Discrete-time Stochastic Linear System

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State Estimation of Discrete Stochastic Singular System

1　 IntroductionIn the area of singular systems control much work has been done recently. The basictheory of singular systems has been proposed. A lot of results are obtained fordeterministic singluar systems[1～ 2 ] . However,only a few results are from stochasticsingular systems[3～ 5] ,in which attention is paid to time-invariant stochastic singularsystems. The state estimation and observation problems of time-invariant stochasticsingular systems have been discussed in[3 ] .The state estim…     

离散时间随机系统的稳定性分析

对由定常非线性随机差分模型所定义状态空间为一般可测空间的离散时间随机系统，本文应用一般状态马氏链遍历性有关理论分析了系统的稳定性问题，给出了由系统相应确定性部分的Ｌｙａｐｕｎｏｖ函数来判别系统稳定的若干充分条件。     

认知无线电网络中授权用户参数估计

针对认知用户在频谱切换过程中无法实时地获取授权用户到达率与服务率的问题,提出了基于隐式马尔可夫模型的参数估计算法。首先利用排队论对授权用户队列进行建模与分析,推导出授权用户队列状态转移概率;其次利用能量感知算法检测授权用户队列真实状态,获得可观测序列值;然后利用隐式马尔可夫模型描述两种随机过程,即授权用户队列状态变化随机过程和可观测序列随机过程;最后利用forward-backward算法估计隐式马尔可夫模型,从而获得授权用户到达率与服务率。仿真结果表明,该方法能够实现实时的、较为精确的估计,从而实时地为认知用户选择频谱切换策略提供依据。     

基于有限单元的贝叶斯滤波估计算法

现代工程系统具有较强的非线性特性,针对这类非线性系统的状态估计问题,提出基于有限单元的贝叶斯原理估计的非线性滤波方法。采用有限单元法逼近系统状态的先验概率解,即前向Kolmogorov方程的解,通过贝叶斯估计得到状态的后验信息。将其方法应用到惯性/地形组合导航系统中,仿真结果表明该方法的可行性。     

Observer-based controller design for singular stochastic Markov jump systems with state dependent noise

This paper is concerned with the problem of observer-based controller design for singular stochastic Markov jump systems with state-dependent noise. Two concepts called “non-impulsiveness” and “mean square admissibility” are introduced, which are different from previous ones. Sufficient conditions for the open- and closed-loop singular stochastic Markov jump systems with state-dependent noise to be mean square admissible are provided in terms of strict LMIs. The controller gain and the observer gain which guarantee the resulting closed-loop error system to be mean square admissible are obtained in turn by solving the strict LMIs. A numerical example is presented to show the efficiency of the design approach.     

基于状态相关模态切换混合估计的航迹预测

针对航空器飞行轨迹预测的随机线性混杂系统估计问题,提出一种状态相关模态切换的混合估计算法(SDTHE). 该算法不仅解决了标准交互式多模型(IMM)算法似然函数为零均值高斯函数假设的缺陷,而且基于实时状态更新模态转移矩阵,使得飞行模态估计更为准确,从而提高飞行轨迹预测的精度. 与标准交互式多模型算法相比,仿真结果表明了所提出算法的有效性和优越性.     

A GMRP Approach for Reliability Analysis of Repairable Systems

We propose here a mathematical approach for the study of repairable systems with arbitrary distributions. The idea is to define a new type of stochastic process, called a generalized Markov renewal process (GMRP). which may describe the transition behavior of the stochastic process at non-regenerative points. In the paper an analytical method for the GMRP is put forward and the formulas are then presented for reliability analysis of repairable systems which can be described by a GMRP with finite states. A signal flow graph technique for system modeling is also summarized here. Finally- an analytical model to evaluate the reliability of a m-out-of- n.G system with general repair-time distribution is developed by means of the GMRP approach.     

Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.     

基于LMIs方法-不确定性随机分布型时滞Markov跳变系统的鲁棒镇定

研究了一类具有不确定性Markov跳变参数的线性分布型时滞系统.考虑了具不确定性、Markov跳变参数、及分布型时滞系统的鲁棒均方稳定性问题.设计了系统的鲁棒状态反馈镇定控制器.采用了Grownwall Bellman不等式,基于线性矩阵不等式(LMIs)方法,给出了鲁棒稳定性的LMI时滞依赖的代数判据,从而降低了判据的保守性.同时得到了相应的离散时滞情形下的结果.     

Robust H∞ control for networked control systems

The robust H∞ control for networked control systems with both stochastic network-induced delay and data packet dropout is studied.When data are transmitted over network,the stochastic data packet dropout process can be described by a two-state Markov chain.The networked control systems with stochastic network-induced delay and data packet dropout are modeled as a discrete time Markov jump linear system with two operation modes.The sufficient condition of robust H∞ control for networked control systems stabilized by state feedback controller is presented in terms of linear matrix inequality.The state feedback controller can be constructed via the solution of a set of linear matrix inequalities.An example is given to verify the effectiveness of the method proposed.     

A New Approach to Optimal Filtering for SingularDiscrete-Time Stochastic Linear System

1IntroductionBecausetherealisticproblemwhichsingularsystemdescribesiswiderthanthenormalsystemsdo,theresearchofsingularsystemshasreceivedagreatdealofattention.Andmanyachievementshavebeenobtainedinthefieldsofstructuralcharacteranalysisanddesignmethodsofsingularsystems.Butforthestateestimationonlypreliminaryprobehasbeenmade.References[2]--[51proposedrespectivesolutionsbasedonleastsquaremethod.References[6]--[71transformsingularsystemsintonormalsystemsthroughmatrixresolutionandthenmakeuseofKalman…     

一类带有随机参数的交通优化模型及遗传算法

双向编组站包含两个个独立的作业系统，确定最合理的系统分工方案是这类编组站必须解决的首要问题，车流量则是该问题重要的输入参数，在实际运输工作中，车流量随着时间的不同总要发生一定的波动变化，这必然会影响双向编组站系统分工方案，针对这一交通运输学科经常面临的情况，构造了车流波动条件下双向编组站系统作为分工的随机优化模型，建立了基于遗传算法的求解理论，该方法对交通问题具有一般性的意义。     

Scenario-based stochastic resource allocation with uncertain probability parameters

The stochastic resource allocation (SRA) problem is an extensive class of combinatorial optimization problems widely existing in complex systems such as communication networks and unmanned systems. In SRA, the ability of a resource to complete a task is described by certain probability, and the objective is to maximize the reward by appropriately assigning available resources to different tasks. This paper is aimed at an important branch of SRA, that is, stochastic SRA (SSRA) for which the probability for resources to complete tasks is also uncertain. Firstly, a general SSRA model with multiple independent uncertain parameters (GSSRA-MIUP) is built to formulate the problem. Then, a scenario-based reformulation which can address multi-source uncertainties is proposed to facilitate the problem-solving process. Secondly, in view of the superiority of the differential evolution algorithm in real-valued optimization, a discrete version of this algorithm was originally proposed and further combined with a specialized local search to create an efficient hybrid optimizer. The hybrid algorithm is compared with the discrete differential evolution algorithm, a pure random sampling method, as well as a restart local search method. Experimental results show that the proposed hybrid optimizer has obvious advantages in solving GSSRA-MIUP problems.     

H 2/H ∞ control problems of backward stochastic systems

This paper is concerned with the mixed H2/H∞ control problem for a new class of stochastic systems with exogenous disturbance signal.The most distinguishing feature,compared with the existing literatures,is that the systems are described by linear backward stochastic differential equations（BSDEs）.The solution to this problem is obtained completely and explicitly by using an approach which is based primarily on the completion-of-squares technique.Two equivalent expressions for the H2/H∞ control are presented.Contrary to forward deterministic and stochastic cases,the solution to the backward stochastic H2/H∞ control is no longer feedback of the current state;rather,it is feedback of the entire history of the state.     

Constrained predictive control of nonlinear stochastic systems

The receding horizon control (RHC) problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs. In the given receding horizon, for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter, the cost function is optimized by constraints on state trajectories, so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set. Out of the receding horizon, the stability is guaranteed by searching a state feedback control law. Based on such stability analysis, a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed. The simulation shows the validity of this method.         

Robust H∞ control for networked control systems

The robust H∞ control for networked control systems with both stochastic network-induced delay and data packet dropout is studied. When data are transmitted over network, the stochastic data packet dropout process can be described by a two-state Markov chain. The networked control systems with stochastic network-induced delay and data packet dropout are modeled as a discrete time Markov jump linear system with two operation modes. The sufficient condition of robust H∞ control for networked control systems stabilized by state feedback controller is presented in terms of linear matrix inequality. The state feedback controller can be constructed via the solution of a set of linear matrix inequalities. An example is given to verify the effectiveness of the method proposed.