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$H_{infty}$ State Estimation for Discrete-Time Nonlinear Singularly Perturbed Complex Networks Under the Round-Robin Protocol

机译: $ H _ { infty} $ 离散时间非线性奇异摄动复杂网络的状态估计轮循协议

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This paper investigates the H-infinity state estimation problem for a class of discrete-time nonlinear singularly perturbed complex networks (SPCNs) under the Round-Robin (RR) protocol. A discrete-time nonlinear SPCN model is first devised on two time scales with their discrepancies reflected by a singular perturbation parameter (SPP). The network measurement outputs are transmitted via a communication network where the data transmissions are scheduled by the RR protocol with hope to avoid the undesired data collision. The error dynamics of the state estimation is governed by a switched system with a periodic switching parameter. A novel Lyapunov function is constructed that is dependent on both the transmission order and the SPP. By establishing a key lemma specifically tackling the SPP, sufficient conditions are obtained such that, for any SPP less than or equal to a predefined upper bound, the error dynamics of the state estimation is asymptotically stable and satisfies a prescribed H-infinity performance requirement. Furthermore, the explicit parameterization of the desired state estimator is given by means of the solution to a set of matrix inequalities, and the upper bound of the SPP is then evaluated in the feasibility of these matrix inequalities. Moreover, the corresponding results for linear discrete-time SPCNs are derived as corollaries. A numerical example is given to illustrate the effectiveness of the proposed state estimator design scheme.
机译:本文研究了基于轮询(RR)协议的一类离散时间非线性奇摄动复杂网络(SPCN)的H无穷状态估计问题。首先在两个时标上设计了离散时间非线性SPCN模型,其差异由奇异摄动参数(SPP)反映。网络测量输出是通过通信网络传输的,在该通信网络中,RR协议对数据传输进行了调度,希望避免不必要的数据冲突。状态估计的误差动态由具有周期性切换参数的切换系统控制。构造了既依赖于传输顺序又依赖于SPP的新颖的Lyapunov函数。通过建立专门解决SPP的关键引理,可以获得足够的条件,使得对于小于或等于预定义上限的任何SPP,状态估计的误差动态渐近稳定并满足规定的H-无穷大性能要求。此外,借助于对一组矩阵不等式的解,给出了期望状态估计量的显式参数化,然后在这些矩阵不等式的可行性中评估了SPP的上限。此外,线性离散时间SPCN的相应结果作为推论得出。数值例子说明了所提出的状态估计器设计方案的有效性。

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