Online Learning ARMA Controllers with Guaranteed Closed-Loop Stability

dc.contributor.author	Sahin, S.
dc.contributor.author	Guzelis, C.
dc.date.accessioned	2021-01-25T20:49:09Z
dc.date.available	2021-01-25T20:49:09Z
dc.date.issued	2016
dc.identifier	10.1109/TNNLS.2015.2480764
dc.identifier.issn	2162237X
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027705223&doi=10.1109%2fTNNLS.2015.2480764&partnerID=40&md5=efc05d6ebe6dce4d626b58dec27596e1
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/10161
dc.description.abstract	This paper presents a novel online block adaptive learning algorithm for autoregressive moving average (ARMA) controller design based on the real data measured from the plant. The method employs ARMA input-output models both for the plant and the resultin
dc.language.iso	English
dc.publisher	IEEE Transactions on Neural Networks and Learning Systems
dc.title	Online Learning ARMA Controllers with Guaranteed Closed-Loop Stability
dc.type	Article
dc.relation.firstpage	2314
dc.relation.lastpage	2326
dc.relation.volume	27
dc.relation.issue	11
dc.description.affiliations	Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Izmir Katip Çelebi University, Izmir, 35620, Turkey; Department of Electrical and Electronics Engineering, Faculty of Engineering, Üniversite Caddesi, Yaşar Uni

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