A nonrepetitive fault estimation design via iterative learning scheme for nonlinear systems with iteration-dependent references

This paper investigates the fault estimation problem for a class of nonlinear nonrepetitive systems subject to iteration-dependent references. Firstly, based on the high-order internal model strategy, iterative learning fault estimation scheme is proposed to track the fault signals that varies with iteration index increasing. Then, the convergence of the presented method is achieved by the norm-based approach. Further, the proposed method is also extended to the uncertain systems with varying parameter matrices, discrete-time systems with Lipschitz perturbation and time-variant coefficients. Finally, the effectiveness of the proposed iterative learning fault estimation scheme is verified by numerical simulation studies.