Event‐Triggered State Estimation of Discrete‐Time Stochastic Coupling Delayed Complex Networks: A Partial‐Node–Based Approach
V. Gokulakrishnan · Mathematical Methods in the Applied Sciences · 2025
ABSTRACT This study addresses the challenge for state estimation of discrete‐time stochastic coupling delayed complex networks (SCDCNs) using partial‐node–based approach. In harsh environments where the measured values of certain network nodes are not available, estimation is performed using only the partial nodes of the networks. The research develops pinning event‐triggered mechanism (PETM) to facilitate state estimation in discrete‐time SCDCNs. By utilizing the Lyapunov‐Krasovskii functional, new sufficient criteria are derived to guarantee the mean square exponentially ultimately bounded (MSEUB) performance of estimation error system via linear matrix inequality. Then, the impacts of estimator gains on MSEUB is investigated. Lastly, a simulation example illustrates the efficiency of designed PETM.