Unified iterative learning control for flexible structures with input constraints

This paper proposes a unified framework of iterative learning control for typical flexible structures under spatiotemporally varying disturbances. Input constraints and the external disturbances are smoothly tackled through hyperbolic tangent functions. Boundary iterative learning control (BILC) law...

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Bibliographic Details
Published inAutomatica (Oxford) Vol. 96; pp. 326 - 336
Main Authors He, Wei, Meng, Tingting, He, Xiuyu, Ge, Shuzhi Sam
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.10.2018
Subjects
Boundary iterative learning control
Distributed parameter system
Disturbance rejection
Flexible structure
Input constraint
Boundary iterative learning control
Disturbance rejection
Flexible structure
Input constraint
Distributed parameter system
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Summary:This paper proposes a unified framework of iterative learning control for typical flexible structures under spatiotemporally varying disturbances. Input constraints and the external disturbances are smoothly tackled through hyperbolic tangent functions. Boundary iterative learning control (BILC) laws are proposed to guarantee the learning convergence. The closed-loop systems can converge to zero along the iteration axis on the basis of time-weighted Lyapunov–Krasovskii-like composite energy functions (CEF). Simulations are implemented to illustrate the effectiveness of the proposed BILC schemes.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2018.06.051