Fault Diagnosis of Hyperbolic Distributed Parameter System

The problem of iterative learning control for hyperbolic distributed parameter systems with faults under sensor/actuator networks is studied. Many results have been achieved in previous studies on iterative learning control, but the problem of fault diagnosis for partial differential systems has rarely been mentioned. Therefore, this paper considers for the first time the occurrence of faults in a class of hyperbolic distributed-parameter systems with fluctuation equations having Kelvin-Voigt damping and viscous damping, and gives the corresponding fault estimation algorithms. An iterative learning based fault diagnosis algorithm and fault estimator are constructed in the article. The convergence of nonlinear system faults in sensor/actuator networks is demonstrated using the principle of compressive mapping. The convergence conditions for the convergence of virtual faults to real faults are obtained through rigorous mathematical analysis. Finally, numerical simulations are carried out according to the convergence conditions, and the numerical simulation results verify the effectiveness of the fault diagnosis algorithm designed in this paper.