Adaptive Fault Estimation for T-S Fuzzy Interconnected Systems Based on Persistent Excitation Condition via Reference Signals

This paper is concerned with the fault estimation (FE) problem for a class of interconnected nonlinear systems described by Takagi-Sugeno fuzzy models. Different from the existing FE approaches, where the reference inputs are always viewed as external disturbances, the considered reference signals are injected directly in the controller to excite the system states to ensure persistent excitation condition. Within this framework, a bank of distributed adaptive observers are then designed to effectively estimate the actuator fault parameters even in the presence of nonlinear interconnections. Moreover, in the disturbance-free case, by employing graph theory, a global Lyapunov function is constructed such that the corresponding fault parameter estimate errors are proved to be asymptotically convergent provided that the interconnected strengths are less than a given constant. Finally, two simulation examples are provided to demonstrate the validity of the presented FE scheme.