Stochastic P-type/D-type iterative learning control algorithms
This paper presents stochastic algorithms that compute optimal and sub-optimal learning gains for a P-type iterative learning control algorithm (ILC) for a class of discrete-time-varying linear systems. The optimal algorithm is based on minimizing the trace of the input error covariance matrix. The...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | article |
| منشور في: |
2003
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/11168 https://doi.org/10.1080/0020717031000077717 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.tandfonline.com/doi/abs/10.1080/0020717031000077717 |
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| _version_ | 1864513488324395008 |
|---|---|
| author | Saab, Samer S. |
| author_facet | Saab, Samer S. |
| author_role | author |
| dc.creator.none.fl_str_mv | Saab, Samer S. |
| dc.date.none.fl_str_mv | 2003 2019-07-29T08:53:39Z 2019-07-29T08:53:39Z 2019-07-29 |
| dc.identifier.none.fl_str_mv | 0020-7179 http://hdl.handle.net/10725/11168 https://doi.org/10.1080/0020717031000077717 Saab, S. S. (2003). Stochastic P-type/D-type iterative learning control algorithms. International Journal of Control, 76(2), 139-148. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.tandfonline.com/doi/abs/10.1080/0020717031000077717 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | International Journal of Control |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Stochastic P-type/D-type iterative learning control algorithms |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper presents stochastic algorithms that compute optimal and sub-optimal learning gains for a P-type iterative learning control algorithm (ILC) for a class of discrete-time-varying linear systems. The optimal algorithm is based on minimizing the trace of the input error covariance matrix. The state disturbance, reinitialization errors and measurement errors are considered to be zero-mean white processes. It is shown that if the product of the input-output coupling matrices C ( t + 1 ) B ( t ) is full column rank, then the input error covariance matrix converges to zero in presence of uncorrelated disturbances. Another sub-optimal P-type algorithm, which does not require the knowledge of the state matrix, is also presented. It is shown that the convergence of the input error covariance matrices corresponding to the optimal and sub-optimal P-type and D-type algorithms are equivalent, and all converge to zero at a rate inversely proportional to the number of learning iterations. A transient-response performance comparison, in the domain of learning iterations, for the optimal and sub-optimal P- and D-type algorithms is investigated. A numerical example is added to illustrate the results. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_96f7d1eca35a7ec664745e67c7e241a2 |
| identifier_str_mv | 0020-7179 Saab, S. S. (2003). Stochastic P-type/D-type iterative learning control algorithms. International Journal of Control, 76(2), 139-148. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/11168 |
| publishDate | 2003 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Stochastic P-type/D-type iterative learning control algorithmsSaab, Samer S.This paper presents stochastic algorithms that compute optimal and sub-optimal learning gains for a P-type iterative learning control algorithm (ILC) for a class of discrete-time-varying linear systems. The optimal algorithm is based on minimizing the trace of the input error covariance matrix. The state disturbance, reinitialization errors and measurement errors are considered to be zero-mean white processes. It is shown that if the product of the input-output coupling matrices C ( t + 1 ) B ( t ) is full column rank, then the input error covariance matrix converges to zero in presence of uncorrelated disturbances. Another sub-optimal P-type algorithm, which does not require the knowledge of the state matrix, is also presented. It is shown that the convergence of the input error covariance matrices corresponding to the optimal and sub-optimal P-type and D-type algorithms are equivalent, and all converge to zero at a rate inversely proportional to the number of learning iterations. A transient-response performance comparison, in the domain of learning iterations, for the optimal and sub-optimal P- and D-type algorithms is investigated. A numerical example is added to illustrate the results.PublishedN/A2019-07-29T08:53:39Z2019-07-29T08:53:39Z20032019-07-29Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article0020-7179http://hdl.handle.net/10725/11168https://doi.org/10.1080/0020717031000077717Saab, S. S. (2003). Stochastic P-type/D-type iterative learning control algorithms. International Journal of Control, 76(2), 139-148.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://www.tandfonline.com/doi/abs/10.1080/0020717031000077717enInternational Journal of Controlinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/111682021-03-19T10:47:36Z |
| spellingShingle | Stochastic P-type/D-type iterative learning control algorithms Saab, Samer S. |
| status_str | publishedVersion |
| title | Stochastic P-type/D-type iterative learning control algorithms |
| title_full | Stochastic P-type/D-type iterative learning control algorithms |
| title_fullStr | Stochastic P-type/D-type iterative learning control algorithms |
| title_full_unstemmed | Stochastic P-type/D-type iterative learning control algorithms |
| title_short | Stochastic P-type/D-type iterative learning control algorithms |
| title_sort | Stochastic P-type/D-type iterative learning control algorithms |
| url | http://hdl.handle.net/10725/11168 https://doi.org/10.1080/0020717031000077717 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.tandfonline.com/doi/abs/10.1080/0020717031000077717 |