Obiekt
Tytuł: An optimal iterative learning control approach for linear systems with nonuniform trial lengths under input constraints
Autor:
Zhuang, Zhihe ; Tao, Hongfeng ; Chen, Yiyang ; Stojanovic, Vladimir ; Paszke, Wojciech (1975- )
Data wydania:
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Abstract:
In practical applications of iterative learning control (ILC), the repetitive process may end up early by accident during the performance improvement along the trial axis, which yields the nonuniform trial length problem. For such practical systems, input signals are usually constrained because of some certain physical limitations. ; This article proposes an optimal ILC algorithm for linear time-invariant multiple-input-multiple-output (MIMO) systems with nonuniform trial lengths under input constraints. The optimal ILC framework is specifically modified for the nonuniform trial length problem, where the prima-dual interior point method is introduced to deal with the input constraints. ; Hence, the constraint handling capability are improved compared with the conventional counterparts for nonuniform trial lengths. Also, the monotonic convergence property of the proposed optimal ILC algorithm is obtained in the sense of mathematical expectation. Finally, the effectiveness of the proposed algorithm is verified on the numerical simulation of a mobile robot.
Opis:
artykuł zamieszczony w: "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
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Licencja CC BY 4.0:
Prawa do dysponowania publikacją:
Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- Repozytorium > Jednostki organizacyjne > Wydział Informatyki, Elektrotechniki i Automatyki
- Repozytorium > Typy utworów > Artykuły
Data ostatniej modyfikacji:
2023-10-25
Data dodania obiektu:
2023-10-19
Liczba wyświetleń treści obiektu:
293
Wszystkie dostępne wersje tego obiektu:
https://zbc.uz.zgora.pl/repozytorium/publication/84031
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| Nazwa wydania | Data |
|---|---|
| An optimal iterative learning control approach for linear systems with nonuniform trial lengths under input constraints | 2023-10-25 |
