Event-triggered adaptive fault estimation for T-S fuzzy systems with actuator faults

This paper is concerned with the event-triggered adaptive fault estimation problem for T-S fuzzy systems with actuator faults and external disturbances. Due to the existence of event-triggered communication (ETC) mechanism, the designed adaptive fault estimation observer and the original fuzzy system cannot share the same premise variables, which makes the existing parallel distributed compensation (PDC)-based fault estimation strategies being infeasible. In order to overcome this difficulty, the deviations of the membership functions between the system and the adaptive observer are injected into the ETC condition. An event-triggered adaptive observer is then designed to jointly estimate the system states and fault parameters. Moreover, it is proved that the estimation errors are uniformly bounded and the network resources are able to be saved. Especially, via the linear matrix inequality (LMI)-based optimization technique, the bounds of the estimation errors are minimized.