Fault estimation for complex networks with model uncertainty and stochastic communication protocol

Abstract

This paper aims to investigate the fault estimation problem for a class of complex networks with model uncertainty and stochastic communication protocol. The model uncertainty existing in the system is norm-bounded. Stochastic communication protocol is employed to cope with possible data collisions in the multiple signal transmissions. An augmented system is constructed by forming an augmented state vector consisting of system states and related faults. By designing the state estimator, the state estimation problem, in the presence of model uncertainty and random disturbance is solved. The parameters of the estimator are obtained by solving several recursive matrix equations such that an upper bound of the estimation error covariance is established and it is minimized. Finally, we give a simulation example to verify the feasibility of the proposed state estimation scheme.

Finite-Horizon H-Infinity Control for Time-Varying State-Saturated Systems under Stochastic Communication Protocol

“…e purpose of this study is to design a finite-time domain output feedback controller (11) so that, for a given disturbance suppression level c, positive definite matrix S, and system initial state x 0 , the controlled output z k of the system meets the following H ∞ performance requirements: 12) to ( 14). e positive real number ε k is introduced to describe the scale of measurement interference and to provide more degrees of freedom [20] for the design of the controller:…”

“…erefore, an effective way to avoid data conflicts is to join the communication protocol to manage the rights of each node to access the network. Random communication protocols have been widely studied and applied for their advantages of random access and strong expansibility [10,11]. In [12], the filtering problem of a kind of linear time-invariant system under a random communication protocol was studied, which described the communication process between the sensor and the filter as a Markov chain.…”

Fault estimation for nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering

“…Remark 2.1: In many studies on fault estimation, it is assumed that the ith-order derivative of the fault is zero (Gao et al, 2019;D. Zhang & Liu, 2019), and this type of faults include some common faults such as step faults and ramp faults.…”

“…Over the past decades, fault diagnosis has been recognized as a basic issue to improve the reliability and security of modern networked systems, and it has attracted extensive research attention (He et al, 2013;Qin et al, 2016;Shahnazari & Mhaskar, 2018). As an important part of fault diagnosis, fault estimation plays an important role in achieving the final index of system reliability, and it has been reported in a large number of literatures (Dong et al, 2016;Qiu et al, 2018;D. Zhang & Liu, 2019).…”

Event‐triggered fault estimation and nonfragile synchronization control for multiweighted complex dynamical networks with deception attacks

“…Therefore the fault estimation for complex networks has been another research hotpot. [41][42][43] In Reference 41, the authors concerned about the time-varying fault estimators of complex networks with stochastic communication protocol, which is subject to missing measurements and randomly occurring faults. In Reference 43, the fault estimators was designed for dealing with complex networks in the presences of randomly varying topologies and stochastic inner couplings.…”