Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties
Abstract Some of the most fundamental assumptions in the design of iterative learning control (ILC) for uncertain systems are the strict repetitiveness (i.e. iteration‐independence) of uncertainties in all factors of plant dynamic, external disturbances, initial conditions, and reference trajectory,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | IET Control Theory & Applications |
| Online Access: | https://doi.org/10.1049/cth2.12368 |
| _version_ | 1828066487880908800 |
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| author | Hamid Shokri‐Ghaleh Soheil Ganjefar Alireza Mohammad Shahri |
| author_facet | Hamid Shokri‐Ghaleh Soheil Ganjefar Alireza Mohammad Shahri |
| author_sort | Hamid Shokri‐Ghaleh |
| collection | DOAJ |
| description | Abstract Some of the most fundamental assumptions in the design of iterative learning control (ILC) for uncertain systems are the strict repetitiveness (i.e. iteration‐independence) of uncertainties in all factors of plant dynamic, external disturbances, initial conditions, and reference trajectory, which may not always hold in practical applications. The simultaneous relaxation of all these assumptions is a challenging problem that has been addressed only for delay‐free systems. This problem is still open for time‐delay systems, especially when the time‐delay factor also has non‐repetitive (i.e. iteration‐dependent/varying) uncertainty. Hence, this study extends the problem of robust ILC for a class of time‐delay systems under nonrepetitive uncertainties in not only plant dynamic, external disturbances, initial conditions, and reference trajectory but also time‐delay. By using the ILC scheme introduced in this work, and based on the frequency domain analysis, it is shown that both monotonic convergence and boundedness of the expected tracking error can be achieved (in the sense of L2‐norm) when the non‐repetitiveness (i.e. iteration‐dependence) of all existing uncertainties from a random viewpoint are taken into account. The effectiveness of the proposed strategy is verified by two simulation examples. |
| first_indexed | 2024-04-10T23:32:29Z |
| format | Article |
| id | doaj.art-1a1e74261a2a425d9f65384db2c9a34c |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-04-10T23:32:29Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-1a1e74261a2a425d9f65384db2c9a34c2023-01-12T04:30:37ZengWileyIET Control Theory & Applications1751-86441751-86522023-01-0117214415910.1049/cth2.12368Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertaintiesHamid Shokri‐Ghaleh0Soheil Ganjefar1Alireza Mohammad Shahri2School of Electrical Engineering Iran University of Science and Technology Tehran IranSchool of Electrical Engineering Iran University of Science and Technology Tehran IranSchool of Electrical Engineering Iran University of Science and Technology Tehran IranAbstract Some of the most fundamental assumptions in the design of iterative learning control (ILC) for uncertain systems are the strict repetitiveness (i.e. iteration‐independence) of uncertainties in all factors of plant dynamic, external disturbances, initial conditions, and reference trajectory, which may not always hold in practical applications. The simultaneous relaxation of all these assumptions is a challenging problem that has been addressed only for delay‐free systems. This problem is still open for time‐delay systems, especially when the time‐delay factor also has non‐repetitive (i.e. iteration‐dependent/varying) uncertainty. Hence, this study extends the problem of robust ILC for a class of time‐delay systems under nonrepetitive uncertainties in not only plant dynamic, external disturbances, initial conditions, and reference trajectory but also time‐delay. By using the ILC scheme introduced in this work, and based on the frequency domain analysis, it is shown that both monotonic convergence and boundedness of the expected tracking error can be achieved (in the sense of L2‐norm) when the non‐repetitiveness (i.e. iteration‐dependence) of all existing uncertainties from a random viewpoint are taken into account. The effectiveness of the proposed strategy is verified by two simulation examples.https://doi.org/10.1049/cth2.12368 |
| spellingShingle | Hamid Shokri‐Ghaleh Soheil Ganjefar Alireza Mohammad Shahri Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties IET Control Theory & Applications |
| title | Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties |
| title_full | Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties |
| title_fullStr | Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties |
| title_full_unstemmed | Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties |
| title_short | Robust convergence conditions of iterative learning control for time‐delay systems under random non‐repetitive uncertainties |
| title_sort | robust convergence conditions of iterative learning control for time delay systems under random non repetitive uncertainties |
| url | https://doi.org/10.1049/cth2.12368 |
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