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-infinity norm bound. Based on Lyapunov's stability theory, novel sufficient conditions for H-infinity 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.

Automated summary

This article discusses the adaptive event-triggered fault detection filter problem for nonlinear-networked control systems, presenting a new AET mechanism and deriving novel sufficient conditions for H-infinity performance and stability. The proposed solution effectively addresses component and sensor faults, network-induced delays, uncertainties, external disturbances, and asynchronous premise variables.