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dc.contributor.authorWitczak, Piotr
dc.contributor.authorPatan, Krzysztof
dc.contributor.authorWitczak, Marcin
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.authorJozef, Korbicz
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2016-02-11T07:58:37Z
dc.date.issued2015
dc.identifier.citationWitczak, P., Patan, K., Witczak, M., Puig, V., Jozef, K. A neural network-based robust unknown input observer design: Application to wind turbine. A: IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes. "IFAC-PapersOnLine (volume 48, issue 21, Pages 1-1496): 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 Paris, 2–4 September 2015". París: International Federation of Automatic Control (IFAC), 2015, p. 263-270.
dc.identifier.isbn2405-8963
dc.identifier.urihttp://hdl.handle.net/2117/82807
dc.description.abstractThe paper deals with the problem of robust unknown input observer design for the neural-network based models of non-linear discrete-time systems. Authors review the recent development in the area of robust observers for non-linear discrete-time systems and proposes less restrictive procedure for design the H8 observer. The approach guaranties simultaneously the predefined disturbance attenuation level (with respect to state estimation error) and convergence of the observer. The main advantage of the design procedure is its simplicity. The paper presents an unknown input observer design that reduced to a set of linear matrix inequalities. The final part of the paper presents an illustrative example concerning wind turbine.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherInternational Federation of Automatic Control (IFAC)
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshRobust control
dc.subject.otherObserver
dc.subject.otherFault Diagnosis
dc.subject.otherUnknown Inputs
dc.subject.otherRobustness
dc.subject.otherSystem Identification
dc.subject.otherTakagi-Sugeno systems
dc.subject.otherArtificial Neural Networks
dc.subject.otherSector Non-linearities
dc.titleA neural network-based robust unknown input observer design: Application to wind turbine
dc.typeConference report
dc.subject.lemacXarxes neuronals (Informàtica)
dc.subject.lemacControl de robustesa
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1016/j.ifacol.2015.09.538
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac17405330
dc.description.versionPostprint (author's final draft)
dc.date.lift10000-01-01
local.citation.authorWitczak, P.; Patan, K.; Witczak, M.; Puig, V.; Jozef, K.
local.citation.contributorIFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes
local.citation.pubplaceParís
local.citation.publicationNameIFAC-PapersOnLine (volume 48, issue 21, Pages 1-1496): 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015 Paris, 2–4 September 2015
local.citation.startingPage263
local.citation.endingPage270


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