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A neural network-based robust unknown input observer design: Application to wind turbine
dc.contributor.author | Witczak, Piotr |
dc.contributor.author | Patan, Krzysztof |
dc.contributor.author | Witczak, Marcin |
dc.contributor.author | Puig Cayuela, Vicenç |
dc.contributor.author | Jozef, Korbicz |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2016-02-11T07:58:37Z |
dc.date.issued | 2015 |
dc.identifier.citation | Witczak, 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.isbn | 2405-8963 |
dc.identifier.uri | http://hdl.handle.net/2117/82807 |
dc.description.abstract | The 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.extent | 8 p. |
dc.language.iso | eng |
dc.publisher | International Federation of Automatic Control (IFAC) |
dc.subject | Àrees temàtiques de la UPC::Informàtica |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Robust control |
dc.subject.other | Observer |
dc.subject.other | Fault Diagnosis |
dc.subject.other | Unknown Inputs |
dc.subject.other | Robustness |
dc.subject.other | System Identification |
dc.subject.other | Takagi-Sugeno systems |
dc.subject.other | Artificial Neural Networks |
dc.subject.other | Sector Non-linearities |
dc.title | A neural network-based robust unknown input observer design: Application to wind turbine |
dc.type | Conference report |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.subject.lemac | Control de robustesa |
dc.contributor.group | Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
dc.identifier.doi | 10.1016/j.ifacol.2015.09.538 |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 17405330 |
dc.description.version | Postprint (author's final draft) |
dc.date.lift | 10000-01-01 |
local.citation.author | Witczak, P.; Patan, K.; Witczak, M.; Puig, V.; Jozef, K. |
local.citation.contributor | IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes |
local.citation.pubplace | París |
local.citation.publicationName | 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 |
local.citation.startingPage | 263 |
local.citation.endingPage | 270 |