Event-based reduced-order H estimation for switched complex networks based on T-S fuzzy model

This paper investigates the design of a reduced-order H(infinity )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-infinity 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.