部分测量数据丢失的网络化系统的l2-l∞滤波

在网络系统中,滤波器得到的观测量通过有限带宽的通道进行传送可能发生丢失.本文研究一类具有测量数据部分丢失的网络化系统的l2-l∞滤波器设计问题.测量数据的丢失采用已知概率分布的二进制切换序列来描述.利用线性矩阵不等式方法,设计全阶和降阶滤波器.所设计的滤波器使得滤波误差系统均方指数稳定,而且保证相对于所有有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数字仿真表明设计方法的有效性.     

部分测量数据丢失的网络化系统的l-l滤波

在网络系统中,滤波器得到的观测量通过有限带宽的通道进行传送可能发生丢失.本文研究一类具有测量数据部分丢失的网络化系统的l2-l∞滤波器设计问题.测量数据的丢失采用已知概率分布的二进制切换序列来描述.利用线性矩阵不等式方法,设计全阶和降阶滤波器.所设计的滤波器使得滤波误差系统均方指数稳定,而且保证相对于所有有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数字仿真表明设计方法的有效性.     

基于自适应事件触发和量化的时滞系统分布式滤波

研究基于自适应事件触发机制和量化的时滞系统分布式滤波问题,设计自适应事件触发机制,此触发机制不仅取决于滤波器自身最新释放数据和当前估计值,还取决于自身和邻居节点最新释放数据的误差。相较于固定参数事件触发,自适应事件触发参数根据滤波性能动态变化。考虑传感器精度和网络传输带宽优先,在数据传输前进行量化处理。构造Lyapunov函数并给出滤波误差系统均方指数稳定且满足l₂-l_∞性能指标的充分条件,设计带有数据量化的离散时滞系统分布式l₂-l_∞滤波器,并通过线性矩阵不等式方法求解滤波器参数。仿真实例说明设计的滤波器能够降低数据量化带来的影响,且自适应事件触发机制相较于固定参数事件触发,在保证滤波性能前提下能够降低数据传输频率,节约网络通信资源。     

基于自适应事件触发的时滞系统分布式${l_2}text{-}{l_infty}$滤波

研究一类基于自适应事件触发机制的时滞系统分布式滤波问题.自适应事件触发条件由滤波器自身最新释放数据、当前时刻估计值及邻居节点最新释放数据共同决定.此事件触发机制采用阈值自适应调节方案,阈值参数在保证滤波器性能的前提下根据滤波误差动态变化,最大程度上节约网络通信资源.首先,给出滤波误差系统均方指数稳定的充分条件;其次,构造一个Lyapunov函数来分析滤波误差系统满足l2-l∞的性能指标;再次,设计离散时滞系统分布式l₂-l_∞滤波器,并通过线性矩阵不等式方法求解滤波器参数;最后,通过仿真实例说明滤波器能够降低系统时滞带来的影响,且在保证滤波性能前提下减少通信次数,节约网络资源.     

具有随机通讯时延的网络化系统的l2-l∞滤波

研究具有随机通讯时延的离散网络化系统的l2-l∞滤波器设计问题.采用满足Bernoulli分布随机变量描述测量数据的一步随机时延;利用线性矩阵不等式方法设计线性滤波器,使得滤波误差系统在均方意义下指数稳定,并保证对于所有有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数值仿真表明了所提出设计方法的有效性.     

随机时延离散系统的l2-l∞ 滤波器型迭代学习控制

对于具有随机时廷的一类离散系统的滤波器型迭代学习控制,采用满足Bernoulli分布的二进制序列来描述数据传输的随机时廷,利用线性矩阵不等式方法设计具有随机时廷的l2-l∞滤波器.给出了滤波误差系统均方指数稳定且具有给定的l2-l∞性能的充分条件,分析了滤波器型迭代学习控制收敛的充分条件.仿真实例说明了该设计方法的有效性.     

基于转移依赖Lyapunov函数离散时间切换系统的异步$l_2-l_infty$滤波

主要研究离散时间切换系统在容许路径依赖混合驻留时间(AED-IDT)切换下异步$l_2-l_\infty$滤波器的设计问题.不同于以往的研究结果,提出一种异步转移依赖凸Lyapunov函数,得到了低保守性稳定性判据.所设计Lyapunov函数的创新之处在于其构建不再依赖系统模态,而是依赖当前激活的滤波器模态和刚刚运行结束的滤波器模态.鉴于所设计Lyapunov函数具有凸性质,这为所提出方法提升自由度和灵活性创造了空间.采用转移依赖凸Lyapunov函数和AED-IDT切换策略,能够得到保证滤波误差系统全局一致指数稳定,且具有$l_2-l_\infty$性能的充分条件.在此基础上,提出异步$l_2-l_\infty$滤波器的设计方法.最后,通过一个数值实例和一个切换RLC应用电路来验证所得结果的有效性,并经过仿真实验验证了所提出设计方法能够保证更紧的驻留时间界和较小的滤波误差结果.     

时滞不确定离散系统的鲁棒l2-l∞滤波

将l2-l∞性能指标引入时滞不确定离散时间系统,研究基于这一指标的滤波器设计问题.所研究的对象是同时具有状态时滞和多面体不确定性的离散时间系统.采用线性矩阵不等式技术推导了此类不确定系统鲁棒l2-l∞滤波器存在的充分条件,并将滤波器的设计转化为一个凸优化的求解问题,可以采用较为有效的内点方法进行求解.所设计的滤波器能够保证相对于所有能量有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数值仿真验证了所提出算法的可行性.     

具有分布时滞和随机测量数据丢失的离散切换系统H∞滤波

由于传感器间接失效、信号带宽限制等原因,测量数据丢失和分布时滞成为无线传感器网络中的常见现象,若不能有效地考虑这些现象对系统的影响,则无法保证系统的性能.为此,针对一类具有分布时滞和随机测量数据丢失的离散切换系统研究H_∞滤波问题,采用已知概率的Bernoulli随机序列描述测量数据丢失,构造Lyapunov-krasorskii函数,基于平均驻留时间法和LMI技术提出保证滤波误差系统均方指数稳定且满足H_∞性能的充分条件.数值仿真验证了所提出方法的可行性.     

具有多测量数据包丢失的线性离散时变系统故障检测滤波器设计

研究存在多数据包丢失现象的线性离散时变系统有限时间域内故障检测滤波器(Fault detection filter, FDF)设计问题. 在数据包具有时间戳标记的条件下, 设计基于观测器的FDF作为残差产生器, 构造两类FDF. 其一为H-/H∞-FDF或H∞/H∞-FDF. 定义故障到残差和未知输入到残差的广义传递函数算子, 将此类FDF设计问题转换为随机意义下H-/H∞ 或H∞/H∞性能指标优化问题. 其二为H∞-FDF, 将此类FDF设计问题转化为随机意义下的H∞滤波问题. 采用基于伴随算子的H∞优化方法, 通过求解递推Riccati方程, 得到上述两类FDF设计问题的解析解. 通过算例验证所提方法的有效性.     

Distributed filtering over wireless sensor networks with parameter and topology uncertainties

ABSTRACT

In this paper, the robust distributed filtering problem is investigated for a class of wireless sensor networks with stochastic parameter and topology uncertainties. The local filters collect information not only from itself but also from its neighbouring sensor nodes according to the network topology. A novel robust distributed filter is proposed which takes stochastic parameter and topology uncertainties into full consideration but does not depend on any specific structure of uncertainties. By proper designing of the filter gain, the trace of estimation error covariance is minimised at each time step, where the new stochastic analysis and augmentation transformation techniques are developed to handle the challenges resulting from multiplicative stochastic uncertainties and sparse topology. The corresponding recursive robust distributed filtering algorithm is also presented for real-time online applications. Finally, a simulation study is carried out to illustrate the effectiveness and applicability of our proposed method.     

Distributed consensus estimation for diffusion systems with missing measurements over sensor networks

In this paper, the problem of distributed consensus estimation with randomly missing measurements is investigated for a diffusion system over the sensor network. A random variable, the probability of which is known a priori, is used to model the randomly missing phenomena for each sensor. The aim of the addressed estimation problem is to design distributed consensus estimators depending on the neighbouring information such that, for all random measurement missing, the estimation error systems are guaranteed to be globally asymptotically stable in the mean square. By using Lyapunov functional method and the stochastic analysis approach, the sufficient conditions are derived for the convergence of the estimation error systems. Finally, a numerical example is given to demonstrate the effectiveness of the proposed distributed consensus estimator design scheme.     

Distributed filtering for nonlinear time‐delay systems over sensor networks subject to multiplicative link noises and switching topology

This paper is concerned with the distributed filtering problem for a class of nonlinear time‐delay system over sensor networks subject to multiplicative link noises and switching topology. Both discrete and distributed time delays are included in the system model. Each sensor estimates the system state by means of the measurements not only from itself but also from its neighboring nodes according to an interactive topology. The multiplicative stochastic link noises are taken into consideration to reflect the random perturbations during the information exchanges between sensor nodes. The considered communication topology is switching according to certain predetermined rules. The purpose of the addressed problem is to develop a distributed filtering strategy such that, in the presence of multiplicative stochastic link noises and switching topology, the resulting filtering error dynamics is exponentially stable in the mean square sense and also satisfies the prespecified weighted disturbance attenuation level. In light of the average dwell time technique in combination with stochastic analysis, sufficient conditions are derived for the solvability of the addressed distributed filtering problem, and the desired filtering gains are then obtained through solving certain convex optimization problems. An illustrative simulation example is presented to demonstrate the correctness and applicability of the obtained theoretical results.     

Reduced-order filtering for networks with Markovian jumping parameters and missing measurements

The problem of reduced-order H _∞ filters design for Markovian jumping complex networks with polytopic time-varying transition probability matrices is first addressed in this paper, where the dynamic of each node is described by the sector-bounded nonlinearity. For the measurements, both quantisation and packet dropouts are considered, where each node has its own packet dropout rate. By using the mode- and transition probability-dependent Lyapunov function approach, two sufficient conditions are provided to ensure the stochastic stability and the disturbance attenuation performance of the resulting filtering error system. Then, the mode-independent reduced-order filters design method is proposed, and the filter parameters are given explicitly by linear matrix inequality method. Finally, the effectiveness of the theoretic results presented is illustrated via a numerical example which contains performance comparison of different mode-independent reduced-order filters.     

Distributed clock skew and offset estimation from relative measurements in mobile networks with Markovian switching topology

We analyze a distributed algorithm for the estimation of scalar parameters belonging to nodes in a mobile network from noisy relative measurements. The motivation comes from the problem of clock skew and offset estimation for the purpose of time synchronization. The time variation of the network was modeled as a Markov chain. The estimates are shown to be mean square convergent under fairly weak assumptions on the Markov chain, as long as the union of the graphs is connected. Expressions for the asymptotic mean and correlation are also provided.     

无线传感器网络分布式量化卡尔曼滤波

本文针对无线传感器网络中的目标跟踪问题,研究了分布式量化卡尔曼滤波问题.由于网络中存在能量和带宽限制,传感器传输的数据必须经过量化处理.考虑一个线性离散随机动态系统,首先提出了一种动态Lloyd-Max量化器并设计了其在线更新方案,然后基于贝叶斯原理导出了递归形式的最优量化卡尔曼滤波器,同时给出了一种渐近等价的迭代算法,并进一步分析了量化卡尔曼滤波器的稳定性.最后,仿真结果验证了所设计算法的可行性与有效性.     

传感器网络分布式事件触发多目标估计

本文主要研究无线传感器网络中目标数目已知且固定的一类分布式多目标跟踪问题,提出了一种完全分布式的基于事件触发的测量和通信策略使得每个节点在不需要全局信息的情况下实现估计误差和能量消耗之间的平衡.监测区域存在多个移动目标,传感器能否测量到单个目标由事件触发测量机制和节点的测量半径来综合决定.基于节点和邻居的信息采用k-means聚类算法来解决数据关联问题,同时提出了基于最小迹原则的一致性卡尔曼滤波算法.从理论上证明了该事件触发策略不仅在性能指标上优于基于时间触发的算法,而且在网络中如果存在节点对多目标协同可观,系统估计误差在均方意义下是稳定的.最后给出了仿真例子验证了该算法的有效性和可行性.     

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.