Hybrid Adjusting Variables-Dependent Event-Based Finite-Time State Estimation for Two-Time-Scale Markov Jump Complex...

Area of Science:

• Control Systems Engineering
• Networked Systems
• Stochastic Systems

Background:

• Complex networks are susceptible to noise and require robust state estimation.
• Event-triggered mechanisms reduce data transmission in networked systems.
• Finite-time estimation offers improved transient performance over infinite-time methods.

Purpose of the Study:

• To develop a dynamic event-triggered mechanism (DETM) for finite-time H∞ state estimation.
• To address discrete-time nonlinear two-time-scale Markov jump complex networks.
• To ensure stochastic finite-time boundedness with H∞ performance for the estimation error.

Main Methods:

• A hybrid adjusting variables-dependent DETM incorporating additive and multiplicative adjusting variables.
• Design of a mode-dependent state estimator using the DETM.
• Utilization of a mode-dependent Lyapunov function and singular perturbation parameter.
• Derivation of a matrix-inequalities-based sufficient condition for estimator parameter design.

Main Results:

• A sufficient condition for designing the state estimator parameters was derived.
• The proposed DETM effectively regulates measurement output releases.
• The designed estimator guarantees stochastic finite-time boundedness with H∞ performance.
• The DETM demonstrated resource savings and improved estimation performance in simulations.

Conclusions:

• The developed DETM is effective for finite-time H∞ state estimation in complex networks.
• The proposed approach conserves network bandwidth.
• The method enhances the accuracy and efficiency of state estimation in challenging network environments.