Abstract
This paper considers a general nonsquare multi-input, multi-output iterative learning control (ILC) system with bounded uncertainties, and proposes a disturbance observer-based ILC method. It is shown that ILC with a disturbance observer has a better performance than traditional ILC against nonrepetitive uncertainties. Numerical examples are provided to illustrate the proposed robust ILC conclusions.
This work was supported by the National Science Foundation of China under Grant 61873013 and Grant 61922007.
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Guo, Z., Meng, D. (2021). Disturbance Observer-Based Design and Analysis of Iterative Learning Control with Nonrepetitive Uncertainties. In: Jia, Y., Zhang, W., Fu, Y. (eds) Proceedings of 2020 Chinese Intelligent Systems Conference. CISC 2020. Lecture Notes in Electrical Engineering, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-15-8450-3_77
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DOI: https://doi.org/10.1007/978-981-15-8450-3_77
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