Show simple item record

dc.contributor.authorKhong, SZ
dc.contributor.authorNesic, D
dc.contributor.authorKrstic, M
dc.date.accessioned2020-11-18T02:21:57Z
dc.date.available2020-11-18T02:21:57Z
dc.date.issued2016-12-22
dc.identifier.citationKhong, S. Z., Nesic, D. & Krstic, M. (2016). An extremum seeking approach to sampled-data iterative learning control of continuous-time non linear systems. IFAC-PapersOnLine, 49 (18), pp.962-967. https://doi.org/10.1016/j.ifacol.2016.10.292.
dc.identifier.issn2405-8963
dc.identifier.urihttp://hdl.handle.net/11343/251795
dc.description.abstractIterative learning control (ILC) of continuous-time nonlinear plants with periodic sampled-data inputs is considered via an extremum seeking approach. ILC is performed without exploiting knowledge about any plant model, whereby the input signal is constructed recursively so that the corresponding plant output tracks a prescribed reference trajectory as closely as possible on a finite horizon. The ILC is formulated in terms of a non-model-based extremum seeking control problem, to which local optimisation methods such as gradient descent and Newton are applicable. Sufficient conditions on convergence to a neighbourhood of the reference trajectory are given.
dc.languageEnglish
dc.publisherIFAC Secretariat
dc.titleAn extremum seeking approach to sampled-data iterative learning control of continuous-time non linear systems
dc.typeJournal Article
dc.identifier.doi10.1016/j.ifacol.2016.10.292
melbourne.affiliation.departmentElectrical and Electronic Engineering
melbourne.source.titleIFAC-PapersOnLine
melbourne.source.volume49
melbourne.source.issue18
melbourne.source.pages962-967
melbourne.elementsid1184644
melbourne.contributor.authorNesic, Dragan
dc.identifier.eissn2405-8963
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record