SCIENCE CHINA Information Sciences, Volume 65 , Issue 7 : 179202(2022) https://doi.org/10.1007/s11432-020-3026-4

Fault estimation based on high order iterative learning scheme for systems subject to nonlinear uncertainties

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  • ReceivedFeb 12, 2020
  • AcceptedJul 1, 2020
  • PublishedMar 16, 2021


There is no abstract available for this article.


This work was supported by National Natural Science Foundation of China (Grant Nos. 61803055, 61803140, 61573076, 61633005, 61673076), Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN201800720), Natural Science Foundation of Chongqing (Grant No. cstc2019jcyjmsxmX0222), Fundamental Research Funds for the Central Universities (Grant Nos. JZ2019HGTB0090, JZ2019HGTB0073), and Opening Foundation of the State Key Laboratory of Traction Power (Grant No. TPL1908).


Appendixes A and B.


[1] Gao Z, Cecati C, Ding S X. A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches. IEEE Trans Ind Electron, 2015, 62: 3757-3767 CrossRef Google Scholar

[2] Mao Z , Wang Y , Jiang B , et al.. Fault diagnosis for a class of active suspension systems with dynamic actuators' faults. International Journal of Control, 2016, 14(5): 1160-1172. Google Scholar

[3] Bin Jiang , Chowdhury F N. Fault estimation and accommodation for linear MIMO discrete-time systems. IEEE Trans Contr Syst Technol, 2005, 13: 493-499 CrossRef Google Scholar

[4] J. Shi, H. Xiao, Z. Wang, and D. Zhou. Iterative learning based fault estimation for nonlinear discrete-time systems. in Chinese Control and Decision Conference, 2013. Google Scholar

[5] Tao H, Paszke W, Rogers E. Iterative learning fault-tolerant control for differential time-delay batch processes in finite frequency domains. J Process Control, 2017, 56: 112-128 CrossRef Google Scholar

[6] D. Meng. Convergence conditions for solving robust iterative learning control problems under nonrepetitive model uncertainties. IEEE Transactions on neural networks and learning systems, 2019, 30(6): 1908-1919. Google Scholar