$H_{\infty}$ State Estimation for Discrete-Time Complex Networks With Randomly Occurring Sensor Saturations and Randomly Varying Sensor Delays

Abstract

Abstract-In this paper, the state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring phenomena from the sensor measurements. The randomly occurring phenomena include randomly occurring sensor saturations (ROSSs) and randomly varying sensor delays (RVSDs) that result typically from networked environments. A novel sensor model is proposed to describe the ROSSs and the RVSDs within a unified framework via two sets of Bernoulli distributed white sequences with known conditional probabilities. Rather than the commonly used Lipschitztype function, a more general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that, for all probabilistic sensor saturations and sensor delays, the dynamics of the estimation error is guaranteed to be exponentially mean-square stable and the effect from the exogenous disturbances to the estimation accuracy is attenuated at a given level by means of an H∞-norm. In terms of a novel Lyapunov-Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semi-definite programme method. A simulation example is provided to show the usefulness of the proposed state estimation conditions.

Outlier-resistant l2-l∞ state estimation for discrete-time memristive neural networks with time-delays

“…Similar to the approach used in Ding, Wang, Shen, et al (2012), the saturation function sat(ὴ(s)) can be divided into a linear and a nonlinear part like:…”

Bulletin of the Polish Academy of Sciences: Technical Sciences

“…On the other hand, the nonlinearity is a non-negligible factor influencing the performances of systems. Indeed, the occurrence of nonlinearity holds a random property since nonlinearity may be induced by the random failure or repair of components and the sudden change of network environment [27]. This kind of nonlinearity is generally called random nonlinearity.…”

Finite-horizon H∞ state estimation for time-varying complex networks based on the outputs of partial nodes

“…There are many examples of complex networks in various natural and man-made systems, such as the World Wide Web, genetic networks, power grids and social networks (Albert & Barabási, 2002;Boccaletti et al, 2006;Costa et al, 2011Costa et al, , 2007Khafaf & Jalili, 2019;Pagani & Aiello, 2013). In the past several decades, the dynamic analysis of complex networks has received extensive attention, such as state estimation (Ding et al, 2012;H. Li, 2013;Sheng et al, 2017;Zou et al, 2017), synchronization (Adu-Gyamfi et al, 2018;Tang et al, 2014;X.…”