Iterative learning control for a class of uncertain nonlinear systems with current state feedback

Jian Liu, Yamiao Zhang and Xiaoe Ruan

International Journal of Systems Science, 2019, vol. 50, issue 10, 1889-1901

Abstract: In this paper, the problem of iterative learning control is addressed for a class of uncertain nonlinear systems with unknown input–output coupling parameters. Firstly, we prove that the control system is realisable if and only if all the input–output coupling parameters are nonzero. Secondly, to surmount the obstacle arising from the unknown input–output coupling parameters, a learning based identification technique is developed for the unknown input–output coupling parameters. Thirdly, a control protocol composed of iterative learning control update law and current state feedback is proposed for the controlled system. The findings suggest that the state feedback part does not change the realizability of controlled system and can be used to improve the transient tracking performance. Especially under certain condition, it can guarantee the monotone convergence of ILC process. Finally, simulation examples are provided to illustrate the effectiveness of the proposed control protocol.

Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1080/00207721.2019.1645235

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