Average Filtering Error-Based Event-Triggered Fuzzy Filter Design With Adjustable Gains for Networked Control Systems

This article investigates the event-triggered $\mathcal {H}_{\infty }$ filtering problem for interval type-2 fuzzy networked control systems with data loss. The goal of this article is to design an event-triggered fuzzy filter with adjustable gains to further improve the $\mathcal {H}_{\infty }$ performance of the filtering error system (FES) while saving communication resources. First, an adaptive event-triggered mechanism (ETM) that utilizes the average filtering error to adaptively adjust the triggered threshold is proposed to mitigate network congestion. In contrast to the existing filtering achievements, the proposed adaptive ETM considers the impact of filtering error on data transmission which can achieve the effect of saving more communication resources. Then, an interval stability theory is applied to adjust the filter gains for achieving the purposes of changing the convergence speed of the FES and improving the $\mathcal {H}_{\infty }$ performance. Moreover, under the conditions of imperfect communication links, sufficient criteria for designing the $\mathcal {H}_{\infty }$ filter with adjustable gains are obtained based on the interval stability theory and linear matrix inequalities technique. Finally, the simulation results are utilized for demonstrating the feasibility of the proposed method.