Adaptive Iterative Learning Control for Non‐Strictly Repeatable Systems With Unknown Control Gains
ABSTRACT This work investigates the adaptive iterative learning control (AILC) problem for nonstrictly repeatable systems subject to unknown control gains. Different to the existing results, we novelly transform the unknown control gain into a kind of norm‐bounded uncertainty, based on which a novel...
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| Vydáno v: | International journal of robust and nonlinear control Ročník 35; číslo 13; s. 5519 - 5528 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Hoboken, USA
John Wiley & Sons, Inc
10.09.2025
Wiley Subscription Services, Inc |
| Témata: | |
| ISSN: | 1049-8923, 1099-1239 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | ABSTRACT
This work investigates the adaptive iterative learning control (AILC) problem for nonstrictly repeatable systems subject to unknown control gains. Different to the existing results, we novelly transform the unknown control gain into a kind of norm‐bounded uncertainty, based on which a novel adaptive estimation approach is designed to reject the unknown control gain. Furthermore, to guarantee the learning ability of the controlled system subject to iteration‐varying trial lengths, piecewise parametric update laws are proposed over the desired trial interval. Consequently, the proposed AILC strategy is then established by employing the error‐tracking approach, which is capable of handling the iteration‐varying initial states effectively. The convergence of the control algorithms is analyzed by applying the Lyapunov‐like theory, and two numerical examples are illustrated to verify the proposed control scheme. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1049-8923 1099-1239 |
| DOI: | 10.1002/rnc.7996 |