On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise
Higher-Order Iterative Learning Control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the s...
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| التنسيق: | conferenceObject |
| منشور في: |
2005
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/11216 http://dx.doi.org/10.1109/CDC.2005.1582530 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/1582530 |
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| _version_ | 1864513488373678080 |
|---|---|
| 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 | 2005 2019-08-20T10:50:25Z 2019-08-20T10:50:25Z 2019-08-20 |
| dc.identifier.none.fl_str_mv | 0780395689 http://hdl.handle.net/10725/11216 http://dx.doi.org/10.1109/CDC.2005.1582530 Saab, S. S. (2005, December). On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise. In Proceedings of the 44th IEEE Conference on Decision and Control (pp. 2451-2456). IEEE. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/1582530 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Adaptive control systems -- Congresses Decision making -- Mathematical models -- Congresses System analysis -- Congresses Feedback control systems -- Congresses |
| dc.title.none.fl_str_mv | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise |
| dc.type.none.fl_str_mv | Conference Paper / Proceeding info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject |
| description | Higher-Order Iterative Learning Control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the control error covariance matrix in the presence of measurement noise. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. Consequently, an optimal HO-ILC is automatically reduced to an optimal first-order ILC. The system under consideration is a linear discrete-time varying systems with different relative degree between the input and each output. Furthermore, a suboptimal second-order ILC is proposed for a class of nonlinear systems. Based on a numerical example, it is shown that a compatible suboptimal first-order ILC yields better performance than the proposed suboptimal second-order ILC algorithm. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | LAURepo_d8a14fc3785bf3b1d34dcf733b9fe11d |
| identifier_str_mv | 0780395689 Saab, S. S. (2005, December). On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise. In Proceedings of the 44th IEEE Conference on Decision and Control (pp. 2451-2456). IEEE. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/11216 |
| publishDate | 2005 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement NoiseSaab, Samer S.Adaptive control systems -- CongressesDecision making -- Mathematical models -- CongressesSystem analysis -- CongressesFeedback control systems -- CongressesHigher-Order Iterative Learning Control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the control error covariance matrix in the presence of measurement noise. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. Consequently, an optimal HO-ILC is automatically reduced to an optimal first-order ILC. The system under consideration is a linear discrete-time varying systems with different relative degree between the input and each output. Furthermore, a suboptimal second-order ILC is proposed for a class of nonlinear systems. Based on a numerical example, it is shown that a compatible suboptimal first-order ILC yields better performance than the proposed suboptimal second-order ILC algorithm.Institute of Electrical and Electronic EngineersIEEE Control Systems SocietyN/AIncludes bibliographical references.IEEE2019-08-20T10:50:25Z2019-08-20T10:50:25Z20052019-08-20Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject0780395689http://hdl.handle.net/10725/11216http://dx.doi.org/10.1109/CDC.2005.1582530Saab, S. S. (2005, December). On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise. In Proceedings of the 44th IEEE Conference on Decision and Control (pp. 2451-2456). IEEE.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://ieeexplore.ieee.org/abstract/document/1582530eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/112162021-03-19T10:47:36Z |
| spellingShingle | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise Saab, Samer S. Adaptive control systems -- Congresses Decision making -- Mathematical models -- Congresses System analysis -- Congresses Feedback control systems -- Congresses |
| status_str | publishedVersion |
| title | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise |
| title_full | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise |
| title_fullStr | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise |
| title_full_unstemmed | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise |
| title_short | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise |
| title_sort | On Higher-Order Iterative Learning Control Algorithm in Presence of Measurement Noise |
| topic | Adaptive control systems -- Congresses Decision making -- Mathematical models -- Congresses System analysis -- Congresses Feedback control systems -- Congresses |
| url | http://hdl.handle.net/10725/11216 http://dx.doi.org/10.1109/CDC.2005.1582530 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://ieeexplore.ieee.org/abstract/document/1582530 |