2.50
Hdl Handle:
http://hdl.handle.net/10547/275872
Title:
Iterative learning control with unknown control direction: a novel data-based approach
Authors:
Shen, Dong; Hou, Zhongsheng
Abstract:
Iterative learning control (ILC) is considered for both deterministic and stochastic systems with unknown control direction. To deal with the unknown control direction, a novel switching mechanism, based only on available system tracking error data, is first proposed. Then two ILC algorithms combined with the novel switching mechanism are designed for both deterministic and stochastic systems. It is proved that the ILC algorithms would switch to the right control direction and stick to it after a finite number of cycles. Moreover, the input sequence converges to the desired one under the deterministic case. The input sequence converges to the optimal one with probability 1 under stochastic case and the resulting tracking error tends to its minimal value.
Affiliation:
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Citation:
Shen, D. and Hou, Z. (2011) 'Iterative Learning Control With Unknown Control Direction: A Novel Data-Based Approach' IEEE Transactions on Neural Networks 22 (12):2237-2249
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Journal:
IEEE Transactions on Neural Networks
Issue Date:
2011
URI:
http://hdl.handle.net/10547/275872
DOI:
10.1109/TNN.2011.2175947
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6087286
Type:
Article
Language:
en
ISSN:
1045-9227; 1941-0093
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorShen, Dongen_GB
dc.contributor.authorHou, Zhongshengen_GB
dc.date.accessioned2013-03-25T13:05:29Z-
dc.date.available2013-03-25T13:05:29Z-
dc.date.issued2011-
dc.identifier.citationShen, D. and Hou, Z. (2011) 'Iterative Learning Control With Unknown Control Direction: A Novel Data-Based Approach' IEEE Transactions on Neural Networks 22 (12):2237-2249en_GB
dc.identifier.issn1045-9227-
dc.identifier.issn1941-0093-
dc.identifier.doi10.1109/TNN.2011.2175947-
dc.identifier.urihttp://hdl.handle.net/10547/275872-
dc.description.abstractIterative learning control (ILC) is considered for both deterministic and stochastic systems with unknown control direction. To deal with the unknown control direction, a novel switching mechanism, based only on available system tracking error data, is first proposed. Then two ILC algorithms combined with the novel switching mechanism are designed for both deterministic and stochastic systems. It is proved that the ILC algorithms would switch to the right control direction and stick to it after a finite number of cycles. Moreover, the input sequence converges to the desired one under the deterministic case. The input sequence converges to the optimal one with probability 1 under stochastic case and the resulting tracking error tends to its minimal value.en_GB
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6087286en_GB
dc.rightsArchived with thanks to IEEE Transactions on Neural Networksen_GB
dc.subjectiterative learning control (ILC)en_GB
dc.subjectiterative learning controlen_GB
dc.titleIterative learning control with unknown control direction: a novel data-based approachen
dc.typeArticleen
dc.contributor.departmentState Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, Chinaen_GB
dc.identifier.journalIEEE Transactions on Neural Networksen_GB
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