Event‐based reduced‐order H∞$H_{\infty }$ estimation for switched complex networks based on T‐S fuzzy model

This paper investigates the design of a reduced‐order H∞$H_\infty$ filter for a specific class of switched complex network systems with time‐varying delays. To handle the nonlinear components of the complex network, a T‐S fuzzy model is employed to convert them into a set of linear components. To tackle the issue of heavy network load, a memory‐based adaptive trigger mechanism is proposed. In this study, a reduced‐order H∞$H_\infty$ state estimator is designed using the T‐S fuzzy model. To demonstrate the exponential stability of the error system, a suitable Lyapunov function is constructed based on the Lyapunov stability principle. The parameter matrix of the reduced‐order estimator is determined using the linear matrix inequality and convex linearization method. Additionally, a sufficient condition for the exponential stability of the error system at the suppression level is provided. Finally, the feasibility of the proposed scheme is validated through a simulation experiment. This paper explores the design of reduced‐order H?filter for a specific class of switched complex network systems with time‐varying delays. In this study, a reduced‐order H?state estimator is designed using the T‐S fuzzy model. A sufficient condition for the exponential stability of the error system at the suppression level is also presented.