具有传感器增益退化、随机时延和丢包的分布式融合估计器

研究了具有传感器增益退化、模型不确定性、数据传输时延和丢包的网络化多传感器分布式融合估计问题,模型的不确定性描述为系统矩阵受到随机扰动,传感器增益退化现象通过统计特性已知的随机变量来描述,随机时延和丢包现象存在于局部最优状态估计向融合中心传输的过程中.首先,设计了一种局部最优无偏估计器,然后将传输时延描述为随机过程,并在融合中心端建立符合存储规则的时延-丢包模型,利用最优线性无偏估计方法,导出最小方差意义下的分布式融合估计器.最后,通过算例仿真证明所设计融合估计器的有效性.     

具有Markovian时延与丢包的离散系统的状态估计

网络化控制系统中经常会因网络带宽有限而导致数据包在网络中传输时产生时延甚至丢失.本文主要研究具有Markovian时延与丢包的离散线性系统的状态估计问题.通过在估计器端设置适当长度的缓存器,把具有多状态Markovian时延与丢包的离散定常系统建模成数据包到达过程为两状态Markovian模型的离散时变系统,并基于跳跃线性估计器的思想提出了一类特殊的估计器,即限定接收历史估计器 (FRHE).在最大时延已知时,给出了可选增益的最优RHE设计策略.该策略虽然是次优,却能提供简便的计算.通过与时变Kalman估计器 (TVKE)的仿真对比,表明所提策略的有效性.     

基于线性编码的随机时延和丢包下的状态估计

关于具有数据随机传输时延和丢包的网络化状态估计问题,针对多个测量数据包同时到达远程处理中心的情况,为减轻计算负担,提出了一种线性编码方法对多个测量值进行线性重组进而用以推导估计器,并给出估计器稳定的充分条件.在最小方差原则下通过改变编码参数组合来改变估计器精度,通过算例仿真验证所提估计器的有效性.结果表明,上述估计器能...     

具有传感器增益退化的不确定系统融合估计器

研究具有传感器增益退化、模型不确定性的多传感器融合估计问题, 其中传感器增益退化现象描述为统计特性已知的随机变量, 模型的不确定性描述为系统矩阵受到随机扰动. 设计一种局部无偏估计器结构, 并建立以局部估计器增益为决策变量、以有限时域下融合估计误差为代价函数的优化问题. 在给出标量融合权重时, 考虑到求得最优的局部估计器增益的解析形式较为困难, 通过最小化代价函数的上界得到一组次优的局部估计器增益. 最后通过算例仿真表明了所设计融合估计器的有效性.

     

网络化最优预测状态估计

当网络诱导时延和数据包丢失确定可知时,提出了一种网络化最优预测状态估计器设计方法,能够补偿网络诱导时延和数据包丢失对估计性能的影响,理论分析表明了随着网络诱导时延或数据包丢失的增加,该估计器在获得明显补偿效果的同时预测估计偏差略微递增,并给出了估计系统的稳定性条件,最后通过仿真和实验验证了所提出方法的有效性和理论分析的正确性.     

存在数据包丢失NCS系统的补偿估计器的建模与分析

对同时存在网络诱导时延及数据包丢失的一类网络控制系统进行了研究和分析。针对时延网络系统存在的数据包丢失,设计出包丢失估计补偿器,用补偿器的信息来更新控制器并建立系统模型,从而有效降低了时延和丢包对系统的影响,最终提高系统性能。与此同时,通过李亚普诺夫稳定性理论来对闭环系统进行了稳定性分析,并给出控制器的设计方法。最后,通过了实例仿真,证实了带补偿器和估计器的网络控制系统设计的有效性。     

网络时延在线估计技术与控制器的协同设计

通过在数据包中附加路由信息, 利用路由跟踪的策略, 确定数据包到达目标节点所经过的路由器的数量, 以此给出网络诱导时延的在线估计方法, 从而较好地克服了利用时间戳方法测量网络传输时延时所产生的远程节点与本地节点之间的时间同步问题. 通过对引入的数据包路由信息的设置和网络路由瓶颈的分析, 给出数据包传输周期的下确界和一种简单的数据丢包的判别方法. 最后给出控制器的协同设计步骤, 实验研究验证了本文方法的有效性.     

具有时延和丢包的网络控制系统H_∞状态反馈控制

研究了具有时延和数据包丢失的网络控制系统H∞状态反馈控制问题.考虑了网络控制系统中同时存在时延和数据包丢失的情况.将丢包过程建模为有限状态的马尔可夫过程.在此模型的基础上,利用Lyapunov稳定性理论和线性矩阵不等式方法.给出了保持系统均方稳定且满足H∞性能的控制器存在的充分条件,并给出了相应的设计方法.最后数例仿真结果表明了控制器设计方法的有效性.     

网络控制系统时变采样周期的建模与切换控制*

针对具有双边随机时延和丢包的网络控制系统,首先采用了主动时变采样周期的方法,利用事件和时间驱动相结合方式,传感器的采样周期可实时地跟随网络延时和丢包的变化而改变,克服了长时延和数据包错序的问题。然后将系统建立为统一的切换系统模型,结合基于平均驻留时间的方法,给出了系统状态满足指数稳定的条件,并且描述了其指数衰减率和丢包率之间的定量关系。最后通过数值例仿真验证了本文所提方法的有效性。     

能量和带宽受限下的分布式一致性融合估计器

针对网络化多传感器分布式估计中传感器能量和通信网络带宽约束问题,提出一种基于降低发送频率和数据压缩降维的分布式一致性融合估计算法.为了满足通信网络带宽要求,各传感器节点直接选取局部估计信号的部分分量进行传输;与此同时,各节点随机间歇式发送数据包到其他节点来节省能量.在给定一致性权重下,建立以一致性估计器增益为决策变量,以所有传感器节点有限时域下状态融合估计误差协方差矩阵的迹的和为代价函数的优化问题,基于Lyapunov稳定性理论给出使得融合估计误差在无噪声时渐近稳定的一致性估计器增益存在的充分条件,并通过最小化代价函数的上界得到一组次优的一致性估计器增益值.最后,通过算例仿真验证算法的有效性.     

Optimal filtering for networked systems with stochastic sensor gain degradation

In this paper, the optimal filtering problem is investigated for a class of networked systems in the presence of stochastic sensor gain degradations. The degradations are described by sequences of random variables with known statistics. A new measurement model is put forward to account for sensor gain degradations, network-induced time delays as well as network-induced data dropouts. Based on the proposed new model, an optimal unbiased filter is designed that minimizes the filtering error variance at each time-step. The developed filtering algorithm is recursive and therefore suitable for online application. Moreover, both currently and previously received signals are utilized to estimate the current state in order to achieve a better accuracy. A numerical simulation is exploited to illustrate the effectiveness of the proposed algorithm.     

State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements

This paper is concerned with the state estimation problem for the complex networked systems with randomly occurring nonlinearities and randomly missing measurements. The nonlinearities are included to describe the phenomena of nonlinear disturbances which exist in the network and may occur in a probabilistic way. Considering the fact that probabilistic data missing may occur in the process of information transmission, we introduce the randomly data missing into the sensor measurements. The aim of this paper is to design a state estimator to estimate the true states of the considered complex network through the available output measurements. By using a Lyapunov functional and some stochastic analysis techniques, sufficient criteria are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable in the mean square. Furthermore, the state estimator gain is also obtained. Finally, a numerical example is employed to illustrate the effectiveness of the proposed state estimation conditions.     

Observer‐based H ∞ control for networked systems with bounded random delays and consecutive packet dropouts

This paper is concerned with the H _∞ control problem for a class of systems with bounded random delays and consecutive packet dropouts that exist in both sensor‐to‐controller channel and controller‐to‐actuator channel during data transmission. A new model is developed to describe possible random delays and packet dropouts by two groups of Bernoulli distributed stochastic variables. To avoid the state augmentation, a full‐order observer‐based feedback controller is designed via LMI approach. Based on the Lyapunov theory, a sufficient condition is provided to guarantee the closed‐loop networked system to be asymptotically mean‐square stable and achieve the prescribed H _∞ disturbance‐rejection‐attenuation level. The simulation examples illustrate the effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.     

State estimation for two‐dimensional complex networks with randomly occurring nonlinearities and randomly varying sensor delays

This paper is concerned with the state estimation problem for two‐dimensional (2D) complex networks with randomly occurring nonlinearities and randomly varying sensor delays. To describe the fact that measurement delays may occur in a probabilistic way, the randomly varying sensor delays are introduced in the delayed sensor measurements. The randomly occurring nonlinearity, on the other hand, is included to account for the phenomenon of nonlinear disturbances appearing in a random fashion that is governed by a Bernoulli distributed white sequence with known conditional probability. The stochastic Brownian motions are also considered, which enter into not only the coupling terms of the complex networks but also the measurements of the output systems. Through available actual network measurements, a state estimator is designed to estimate the true states of the considered 2D complex networks. By utilizing an energy‐like function, the Kronecker product and some stochastic analysis techniques, several sufficient criteria are established in terms of matrix inequalities under which the 2D estimation error dynamics is globally asymptotically stable in the mean square. Furthermore, the explicit expression of the estimator gains is also characterized. Finally, a numerical example is provided to demonstrate the effectiveness of the design method proposed in this paper. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal full-Order filtering for discrete-time systems with random measurement delays and multiple packet dropouts

This paper is concerned with the estimation problem for discrete-time stochastic linear systems with possible single unit delay and multiple packet dropouts. Based on a proposed uncertain model in data transmission, an optimal full-order filter for the state of the system is presented, which is shown to be of the form of employing the received outputs at the current and last time instants. The solution to the optimal filter is given in terms of a Riccati difference equation governed by two binary random variables. The optimal filter is reduced to the standard Kalman filter when there are no random delays and packet dropouts. The steady-state filter is also investigated. A sufficient condition for the existence of the steady-state filter is given. The asymptotic stability of the optimal filter is analyzed.     

Distributed fault detection over sensor networks with Markovian switching topologies

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.     

A new approach to distributed fusion filtering for networked systems with random parameter matrices and correlated noises

This paper is concerned with the distributed filtering problem for a class of discrete-time stochastic systems over a sensor network with a given topology. The system presents the following main features: (i) random parameter matrices in both the state and observation equations are considered; and (ii) the process and measurement noises are one-step autocorrelated and two-step cross-correlated. The state estimation is performed in two stages. At the first stage, through an innovation approach, intermediate distributed least-squares linear filtering estimators are obtained at each sensor node by processing available output measurements not only from the sensor itself but also from its neighboring sensors according to the network topology. At the second stage, noting that at each sampling time not only the measurement but also an intermediate estimator is available at each sensor, attention is focused on the design of distributed filtering estimators as the least-squares matrix-weighted linear combination of the intermediate estimators within its neighborhood. The accuracy of both intermediate and distributed estimators, which is measured by the error covariance matrices, is examined by a numerical simulation example where a four-sensor network is considered. The example illustrates the applicability of the proposed results to a linear networked system with state-dependent multiplicative noise and different network-induced stochastic uncertainties in the measurements; more specifically, sensor gain degradation, missing measurements and multiplicative observation noises are considered as particular cases of the proposed observation model.     

The problem of state estimation via asynchronous communication channels with irregular transmission times

We study a linear discrete-time partially observed system perturbed by white noises. The observations are transmitted to the estimator via communication channels with irregular transmission times. Various measurement signals and even parts of a given sensor output may incur independent delays; messages transferred via the channels may be lost or corrupted. The minimum variance state estimation problem is solved. It is shown that the proposed state estimator is exponentially stable under natural assumptions.