Adaptive Event-Triggered Fault Detection for Interval Type-2 T–S Fuzzy Systems With Sensor Saturation

This article deals with the adaptive event-triggered (AET) fault detection filter (FDF) problem for nonlinear-networked control systems with component and sensor faults, network-induced delays, uncertainties, external disturbances, and asynchronous premise variables. This system is represented by the interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model, which can effectively capture parameter uncertainties. A new AET mechanism with many advantages, such as no singular problem, no degradation into a traditional time-triggered mechanism, fewer triggers, and no Zeno behavior, is constructed. The error caused by the AET mechanism is first regarded as a disturbance and thus can be attenuated by the H
_∞
norm bound. Based on Lyapunov's stability theory, novel sufficient conditions for H
_∞
performance and stability are then derived. In addition, the filter parameters and the weight matrix of the trigger condition are obtained in terms of linear matrix inequality (LMI) techniques. Finally, a numerical example is used to demonstrate the feasibility and merit of the proposed fault detection scheme.