Abstract
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.









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Acknowledgements
This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN201800720), the National Natural Science Foundation of China (Grants 61803055, 61803140, 61633005, 61673076), Natural Science Foundation of Chongqing (Grant No. cstc2019jcyjmsxmX0222), the Fundamental Research Funds for the Central Universities JZ2019HGTB0090 and JZ2019HGTB0073, the national key R & D project No.2020yfb2009405 and Equipment research project in advance (41402040301), Chongqing Postgraduate Scientific Research Innovation Project under Grant CYS20285.
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Li, F., Kenan, D., Shuiqing, X. et al. A nonrepetitive fault estimation design via iterative learning scheme for nonlinear systems with iteration-dependent references. Neural Comput & Applic 34, 5169–5179 (2022). https://doi.org/10.1007/s00521-021-06176-3
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DOI: https://doi.org/10.1007/s00521-021-06176-3