Fault diagnosis of time-delay complex dynamical networks using output signals

This paper proposes a novel approach for fault diagnosis of a time-delay complex dynamical network.Unlike the other methods,assuming that the dynamics of the network can be described by a linear stochastic model,or using the state variables of nodes in the network to design an adaptive observer,it only uses the output variable of the nodes to design an observer and an adaptive law of topology matrix in the observer of a complex network,leading to simple design of the observer and easy realisation of topology monitoring for the complex networks in real engineering.The proposed scheme can monitor any changes of the topology structure of a time-delay complex network.The effectiveness of this method is successfully demonstrated by virtue of a complex networks with Lorenz model.     

网络环境下的大系统鲁棒H_∞控制

针对网络环境下的一类同时具有测量数据和控制数据丢失的线性离散大系统,研究其H_∞状态反馈控制器设计问题.大系统由N个线性离散关联子系统构成,假设测量数据和控制数据丢失满足已知概率的Bernoulli分布,采用线性矩阵不等式方法给出了H_∞控制器存在的充分条件,所设计的控制器使得闭环系统均方指数稳定且满足指定的H_∞性能指标.最后通过仿真例子说明该方法的有效性.     

Topology identification for a class of complex dynamical networks using output variables

A problem of topology identification for complex dynamical networks is investigated in this paper. An adaptive observer is proposed to identify the topology of a complex dynamical networks based on the Lyapunov stability theory. Here the output of the network and the states of the observer are used to construct the updating law of the topology such that the communication resources from the network to its observer are saved. Some convergent criteria of the adaptive observer are derived in the form of linear inequality matrices. Several numerical examples are shown to demonstrate the effectiveness of the proposed observer.