Abstract
In order to asymptotically track the desired trajectory of nonlinear systems with unidirectional control input under various uncertainties, a modified saturated learning-based control law with adaptive notch filter (ANF) is presented in this paper. The proposed control law ensures the asymptotic convergence of the tracking error by utilizing a saturated function and a repetitive learning-based estimator, which allows the learning of the periodic dynamic behavior of the nonlinear system online to make compensations. Simultaneously, an ANF is integrated with the proposed control law to estimate the frequency of the periodic dynamics online. Differing from the existing methodologies in the literatures, the control law proposed in this paper only requires the unknown dynamics be bounded (the parameters, structure, and period of the unknown dynamics is not required to be known). Finally, applying the proposed control law to the electromagnetic suspension system, simulation, and experimental results are included to demonstrate the effectiveness of the proposed method.
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27 July 2018
The first affiliation of the author Yougang Sun should be "National Maglev Transportation Engineering R&D Center, Tongji University’’, which can be found as follows.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (grant no. 51505277).
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Sun, Y., Qiang, H., Mei, X. et al. Modified repetitive learning control with unidirectional control input for uncertain nonlinear systems. Neural Comput & Applic 30, 2003–2012 (2018). https://doi.org/10.1007/s00521-017-2983-y
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DOI: https://doi.org/10.1007/s00521-017-2983-y