Piecewise affine identification of a hydraulic pumping system using evolutionary computation

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dc.creatorBruno Henrique Groenner Barbosa
dc.creatorLuis Antonio Aguirre
dc.creatorAntônio de Pádua Braga
dc.date.accessioned2025-04-17T13:27:39Z
dc.date.accessioned2025-09-08T23:17:03Z
dc.date.available2025-04-17T13:27:39Z
dc.date.issued2018
dc.identifier.doihttps://doi.org/10.1049/iet-cta.2018.5621
dc.identifier.issn1751-8644
dc.identifier.urihttps://hdl.handle.net/1843/81688
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofIET Control theory and applications
dc.rightsAcesso Aberto
dc.subjectSistemas não lineares
dc.subjectBombas hidráulicas
dc.subject.otherit is sometimes hard to obtain non-linear models that describe the processes over a wide range of operating conditions
dc.subject.otheralternative ways of representing non-linear systems as a combination of simpler linear models have been developed
dc.titlePiecewise affine identification of a hydraulic pumping system using evolutionary computation
dc.typeArtigo de periódico
local.citation.epage1403
local.citation.issue9
local.citation.spage1394
local.citation.volume13
local.description.resumoIdentification of piecewise affine hybrid systems is not an easy task since both the parameters determining the discrete modes and the submodels parameters have to be obtained, leading to a non-convex combinatorial optimisation problem. One way around this problem is to solve it in two steps. This work presents an approach for simultaneously estimating the parameters for PieceWise Autoregressive eXogeneous and PieceWise Output Error models. This is accomplished by using evolutionary algorithms for finding the parameters of the discrete modes (Gaussian mixture models) and employing the proposed weighted and extended least squares algorithm to estimate the ARX or ARMAX submodels. The main advantages of the proposed algorithm are: (i) it can be applied to output error models – which correspond to system with measurement noise, (ii) it is less likely to get trapped in local minima and (iii) it is applicable to problems with large datasets. The proposed approach, which is offline, was validated using simulated and experimental data and was compared with another method from the literature. In the case of an experimental plant, parsimonious models with good performance in both dynamic and static regimes were obtained.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-cta.2018.5621

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