Hybrid Adjusting Variables-Dependent Event-Based Finite-Time State Estimation for Two-Time-Scale Markov Jump Complex Networks.
- Resource Type
- Academic Journal
- Authors
- Wan X; Yang C; Zhang CK; Wu M
- Source
- Publisher: Institute of Electrical and Electronics Engineeers Country of Publication: United States NLM ID: 101616214 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2162-2388 (Electronic) Linking ISSN: 2162237X NLM ISO Abbreviation: IEEE Trans Neural Netw Learn Syst Subsets: PubMed not MEDLINE; MEDLINE
- Subject
- Language
- English
This article investigates the problem of dynamic event-triggered finite-time H ∞ state estimation for a class of discrete-time nonlinear two-time-scale Markov jump complex networks. A hybrid adjusting variables-dependent dynamic event-triggered mechanism (DETM) is proposed to regulate the releases of measurement outputs of a node to a remote state estimator. Such a DETM contains both an additive dynamically adjusting variable (DAV) and a multiplicative adaptively adjusting variable. The aim is to design a DETM-based mode-dependent state estimator, which guarantees that the resultant error dynamics is stochastically finite-time bounded with H ∞ performance. By constructing a mode-dependent Lyapunov function with multiple DAVs and a singular perturbation parameter associated with time scales, a matrix-inequalities-based sufficient condition is derived, the feasible solutions of which facilitate the design of the parameters of the state estimator. The validity of the designed state estimator and the superiority of the devised DETM are verified by two examples. It is verified that the devised DETM is capable of saving network resources and simultaneously improving the estimation performance.