Resilient state estimation for time-varying uncertain dynamical networks with data packet dropouts and switching topology: an event-triggered method

• Published in: IET control theory & applications Vol. 14; no. 3; pp. 367 - 377
• Main Authors: Gao, Ming, Hu, Jun, Chen, Dongyan, Jia, Chaoqing
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 12.02.2020
• Subjects: addressed time‐varying uncertain complex networks; complex networks; covariance matrices; data packet dropouts; discrete time systems; DPDs; estimator gain perturbations; event‐triggered method; event‐triggered protocol; event‐triggered strategy; matrix algebra; network resources; nonlinear control systems; nonlinearities; optimal estimator gain matrix; provided time‐varying state estimation method; random variables; recursive state estimation method; Research Article; resilient state estimation; RONs; state estimation; state estimation algorithm; state estimation error covariance matrix; switching topology; time‐varying systems; time‐varying uncertain dynamical networks; uncertain systems; time-varying systems; RONs; event-triggered strategy; complex networks; state estimation error covariance matrix; state estimation algorithm; recursive state estimation method; optimal estimator gain matrix; time-varying uncertain dynamical networks; nonlinearities; DPDs; covariance matrices; ST; discrete time systems; provided time-varying state estimation method; uncertain systems; resilient state estimation; switching topology; random variables; event-triggered method; matrix algebra; event-triggered protocol; estimator gain perturbations; nonlinear control systems; data packet dropouts; network resources; state estimation; addressed time-varying uncertain complex networks
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2019.0721

In this study, the recursive state estimation method based on the event-triggered protocol is given for time-varying uncertain complex networks with switching topology (ST), data packet dropouts (DPDs), and randomly occurring non-linearities (RONs). The event-triggered strategy is given to regulate the communications and then save the limited network resources when the measurement information is transmitted. The phenomena of RONs, ST, and DPDs are depicted by some random variables obeying the Bernoulli distribution. Besides, the estimator gain perturbations are dealt with. The major aim of this study is to present a new state estimation algorithm for addressed time-varying uncertain complex networks such that, for all STs, DPDs, and RONs, a minimal upper bound of state estimation error covariance matrix is derived by designing an optimal estimator gain matrix. Finally, a numerical simulation is presented to show the effectiveness of the provided time-varying state estimation method.