Fault Estimation for Switched Interconnected Nonlinear Systems With External Disturbances via Variable Weighted Iterative Learning

The focus of this brief is a fault-estimation issue for switched interconnected nonlinear systems (SINSs) subjected to external disturbances. First, to estimate the fault of all subsystems under external disturbances, a distributed iterative learning observer (DILO) is designed by utilizing related information among subsystems. Then, a novel variable weighted iterative learning (VWIL)-based fault-estimation law is proposed to fast-track the fault signals and weaken the disturbance effects. Subsequently, the convergence conditions are achieved using ?-norm method and mathematical induction. In addition, the gain matrices of the DILO and VWIL law are calculated simultaneously. Finally, simulation results are given to verify the feasibility of the proposed method, which show that accurate fault-estimation results can be obtained after about the 20th iteration.

Automated summary

This paper focuses on the fault estimation issue of switched interconnected nonlinear systems (SINSs) under external disturbances. A distributed iterative learning observer (DILO) is designed to estimate faults of all subsystems under external disturbances by utilizing related information. A novel variable weighted iterative learning (VWIL)-based fault-estimation law is proposed to fast-track fault signals and weaken disturbance effects. Convergence conditions are achieved using the ?-norm method and mathematical induction. The gain matrices of DILO and VWIL law are calculated simultaneously. Simulation results demonstrate the feasibility of the proposed method, showing accurate fault-estimation results can be obtained after about the 20th iteration.