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.     

Synchronisation of discrete-time complex networks with randomly occurring uncertainties,nonlinearities and time-delays

This paper deals with the synchronisation problem for an array of coupled complex discrete-time networks with the presence of randomly occurring information. The time-varying delays, parameter uncertainties and nonlinearities enter into the system in a random way and such randomly occurring time-delays, randomly occurring uncertainties and randomly occurring sector-like nonlinearities obey certain mutually uncorrelated Bernoulli-distributed white-noise sequences. By employing direct delay decomposition approach and constructing suitable Lyapunov–Krasovskii functional, sufficient conditions are established to ensure the synchronisation criteria for the complex networks with randomly occurring information in terms of linear matrix inequalities. Finally, in numerical examples, synchronisation of Barabàsi Albert scale-free networks and chaotic synchronisation of Lorenz system are rendered to exemplify the effectiveness and applicability of the proposed results.     

Dissipative control for state-saturated discrete time-varying systems with randomly occurring nonlinearities and missing measurements

In this paper, the dissipative control problem is investigated for a class of discrete time-varying systems with simultaneous presence of state saturations, randomly occurring nonlinearities as well as multiple missing measurements. In order to render more practical significance of the system model, some Bernoulli distributed white sequences with known conditional probabilities are adopted to describe the phenomena of the randomly occurring nonlinearities and the multiple missing measurements. The purpose of the addressed problem is to design a time-varying output-feedback controller such that the dissipativity performance index is guaranteed over a given finite-horizon. By introducing a free matrix with its infinity norm less than or equal to 1, the system state is bounded by a convex hull so that some sufficient conditions can be obtained in the form of recursive nonlinear matrix inequalities. A novel controller design algorithm is then developed to deal with the recursive nonlinear matrix inequalities. Furthermore, the obtained results are extended to the case when the state saturation is partial. Two numerical simulation examples are provided to demonstrate the effectiveness and applicability of the proposed controller design approach.     

增益随机变化随机丢包时滞系统非脆弱H∞滤波

鉴于目前的数字控制系统是离散的,设计了一类具有无穷分布时滞、随机丢失测量的离散系统的非脆弱H_∞滤波器．为了模拟离散系统增益随机变化现象,在滤波器的增益中引入高斯分布的随机变化分量;引入无穷分布时滞,刻画网络控制系统中有限的带宽产生的影响;进一步地,考虑由伯努利分布的白序列描述的随机丢失测量现象．基于李亚普诺夫稳定性理论、随机分析技术及线性矩阵不等式（LMI）技术,得到使整个滤波器增广系统渐近稳定,同时满足性能指标的非脆弱H_∞滤波器存在的充分条件,通过半定程序方法求解滤波器增益．最后,由数值仿真说明研究的有效性．     

Distributed control of cluster synchronisation in networks with randomly occurring non-linearities

This paper is concerned with the issue of mean square cluster synchronisation in complex networks, which consist of non-identical nodes with randomly occurring non-linearities. In order to guarantee synchronisation, distributed controllers depending on the information from the neighbours in the same cluster are applied to each node, meanwhile, the control gains are supposed to be updated according to the given laws. Based on the Lyapunov stability theory, the sufficient synchronisation conditions are derived and proved theoretically. Finally, a numerical example is presented to demonstrate the effectiveness of the results.     

基于域间路由的分布式分组过滤有效性研究

消除伪造源地址分组是互联网安全可信的内在要求.基于路由的分布式分组过滤具有良好的效果,但是目前对其有效性缺乏严密的理论分析.基于域间路由传播和互联网拓扑的分层特征,建立路由传播数模型和理想AS图模型,以此为工具分析了基于域间路由的最大过滤和半最大过滤有效性.结论印证并从理论上解释了前人研究中的实验结果.最大过滤能够消除绝大多数的伪造分组,虽然无法达到100％,但可以将伪造成功的自治系统数量限制为互联网AS路径的平均长度.在理想AS图上,半最大过滤与最大过滤的有效性相同,但是存储和计算开销要小很多,为实际中部署半最大过滤提供了理论依据.理论模型分析揭示了基于域间路由的分布式分组过滤的内在优缺点,有助于设计辅助措施和在整个互联网全面而合理地部署.     

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.     

基于无线传感器网络的分布递阶信息融合算法

提出了基于无线传感器网络的分布递阶信息融合方法,下层源节点采用卡尔曼滤波及基于减少能耗和网络冲突的数据处理方法,上层汇聚节点采用方差最小的加权信息融合方法,该方法能有效降低传感器网络能耗和网络信息冲突,仿真结果表明了该方法的有效性和可靠性。