The distributed H infinity resilientstateestimation problem of nonlinear discrete systems in sensor networks is investigated in this article. The system model under consideration involves three phenomena of incomple...
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The distributed H infinity resilientstateestimation problem of nonlinear discrete systems in sensor networks is investigated in this article. The system model under consideration involves three phenomena of incomplete information: randomly occurring nonlinearities, fading measurements, and random gain variations. The probabilistic characteristics of the above phenomena are depicted by three sets of independent random variables subject to more general random distribution. Based on the above model, by applying Lyapunov functional approach and random distribution solution method, the asymptotic stability in the mean square sense of the estimation error system with a given H infinity attenuation level is proved. Further, the estimator parameters are solved by introducing a novel linearization method. Finally, a numerical simulation is given to illustrate the validity of the theoretical results.
This article is concerned with the distributed H-infinity resilientstateestimation problem for a class of nonlinear systems with randomly occurring communication delays and missing measurements in sensor networks. A...
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This article is concerned with the distributed H-infinity resilientstateestimation problem for a class of nonlinear systems with randomly occurring communication delays and missing measurements in sensor networks. Anovel sensor model is proposed, in which two Bernoulli distributed white sequences are introduced to describe the random communication delay and missing measurements in a unified framework. Meanwhile, the estimator gain is allowed to fluctuate within a certain range. Based on the developed model, a novel Lyapunov-Krasovskii functional with multiple delay information terms is constructed, then the stochastic analysis technique and the extended integral inequality are used to calculate the functional derivative. Consequently, the existence conditions for the required distributed estimator are established to ensure that the estimation error system is asymptotically mean-square stable and satisfies the prescribed H-infinity performance constraint, and the desired gain of distributedresilient estimator is also solved by linearizing the nonlinear terms. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.
In this paper, the distributed resilient state estimation problem is discussed for fractional-order state-saturated complex networks with nonlinearities and fading measurements. The nonlinear function is assumed to sa...
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ISBN:
(纸本)9798331540845;9789887581598
In this paper, the distributed resilient state estimation problem is discussed for fractional-order state-saturated complex networks with nonlinearities and fading measurements. The nonlinear function is assumed to satisfy the Lipschitz condition and the fading measurements appear in a stochastic manner with the related coefficients depicted by random variables with known expectations. The distributedresilient estimator gain perturbations are modelled by norm-bounded uncertainties. In particular, based on certain assumption with respect to the cross term of estimation errors, an upper bound (UB) of the estimation error covariance is derived through solving two groups of matrix difference equations. Subsequently, the distributedresilient estimator gain is parameterized by minimizing the trace of the obtained UB. In the end, a numerical simulation is given to verify the validity of the designed distributed resilient state estimation scheme.
This article investigates the problem of event-triggered distributedresilientstate estimator (ET-DRSE) for nonlinear cyber-physical systems (NCPSs) under hybrid cyberattacks. A stochastic model for hybrid cyberattac...
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This article investigates the problem of event-triggered distributedresilientstate estimator (ET-DRSE) for nonlinear cyber-physical systems (NCPSs) under hybrid cyberattacks. A stochastic model for hybrid cyberattacks is constructed by considering both false data injection (FDI) and Denial-of-Service (DoS) attacks. By proposing an event-triggered mechanism, the network burden is reduced by decreasing the transmission rate of measurement information. To improve the flexibility, a detector-based distributedresilientstate estimator framework is derived. Coupled Riccati-like difference equations (CRDEs) are proposed to obtain an upper bound (UB) of the estimation error covariance (EEC) of every subsystem. An estimation gain is designed to minimize the UB upon the error covariance of the resilient estimator by recursively solving the CRDEs. It is proved that the estimation error is probabilistically bounded through the application of random analytic theory. Finally, the performance of the proposed ET-DRSE is verified through a numerical example and a chemical process example.
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