Event-triggered state estimation for time-delayed complex networks with gain variations based on partial nodes

Abstract

This paper designs the event-triggered non-fragile state estimator for a class of time-delayed complex networks with randomly occurring sensor saturations (ROSSs) and estimator gain variations on the basis of measurements from partial nodes. Both the time-varying state delays and the stochastic disturbances are considered in the network model. A Bernoulli-distributed white sequence is utilized to reflect the phenomenon of ROSSs. Two sequences of Gaussian distributed random variables combined with the multiplicative norm-bounded uncertainties are used to characterize the randomly occurring gain variations in the estimators. An event generator function is employed to regulate the transmission of data from the sensor to the estimator. The aim of this paper is to design an exponentially ultimately bounded state estimator in mean square through measurement outputs from a partial of network nodes under the event-triggered mechanism. With the help of Lyapunov–Krasovskii functional and stochastic analysis techniques, sufficient conditions are acquired for the existence of the desired state estimator which ensures that the estimation error dynamics is exponentially ultimately bounded in mean square, and then the estimator gain matrices can be computed via the software Matlab. A simulation example is provided to demonstrate the effectiveness of the proposed state estimation method.

Keywords:

• Event-triggered mechanism
• non-fragile state estimation
• complex networks
• state delays
• measurements from partial nodes
• randomly occurring sensor saturations

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant number 61422301], [grant number 61374127], [grant number 61473183], [grant number U1509211]; the Northeast Petroleum University Innovation Foundation For Postgraduate [YJSCX2017-026NEPU] and the Alexander von Humboldt Foundation of Germany.