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dc.contributor.authorBessa, Wallace Moreira-
dc.contributor.authorBrinkmann, Gerrit-
dc.contributor.authorDücker, Daniel-André-
dc.contributor.authorKreuzer, Edwin-
dc.contributor.authorSolowjow, Eugen-
dc.date.accessioned2019-01-11T10:20:45Z-
dc.date.available2019-01-11T10:20:45Z-
dc.date.issued2018-12-30-
dc.identifier.citationWallace M. Bessa, Gerrit Brinkmann, Daniel A. Duecker, Edwin Kreuzer, and Eugen Solowjow, “A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent,” Mathematical Problems in Engineering, vol. 2018, Article ID 9648126, 16 pages, 2018. doi:10.1155/2018/9648126de_DE
dc.identifier.issn1563-5147de_DE
dc.identifier.urihttp://dx.doi.org/10.1155/2018/9648126-
dc.identifier.urihttps://tubdok.tub.tuhh.de/handle/11420/1971-
dc.description.abstractMechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.en
dc.description.sponsorshipDFG [Kr752/33-1, Kr752/36-1] u.a.de_DE
dc.language.isoende_DE
dc.publisherHindawi Publishing Corporationde_DE
dc.relation.ispartofMathematical problems in engineeringde_DE
dc.rightsCC BY 4.0de_DE
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.subject.ddc620: Ingenieurwissenschaftende_DE
dc.titleA biologically inspired framework for the intelligent control of mechatronic systems and its application to a micro diving agentde_DE
dc.typeArticlede_DE
dc.date.updated2019-01-06T07:11:40Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066en-
dc.rights.holderCopyright © 2018 Wallace M. Bessa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.-
dc.identifier.urnurn:nbn:de:gbv:830-882.025328-
dc.identifier.doi10.15480/882.1968-
dc.type.diniarticle-
dc.subject.ddccode620-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.025328-
tuhh.oai.showtruede_DE
dc.identifier.hdl11420/1971-
tuhh.abstract.englishMechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.de_DE
tuhh.relation.ispartofMathematical Problems in Engineering-
tuhh.publisher.doihttps://doi.org/10.1155/2018/9648126-
tuhh.publication.instituteMechanik und Meerestechnik M-13de_DE
tuhh.identifier.doi10.15480/882.1968-
tuhh.type.opus(wissenschaftlicher) Artikelde
tuhh.institute.germanBiomechanik M-3de
tuhh.institute.englishMechanik und Meerestechnik M-13de_DE
tuhh.gvk.hasppnfalse-
openaire.rightsinfo:eu-repo/semantics/openAccessde_DE
dc.type.driverarticle-
dc.rights.ccbyde_DE
dc.rights.ccversion4.0de_DE
dc.type.casraiJournal Articleen
tuhh.container.volume24.2018de_DE
tuhh.container.startpageArticle ID 9648126de_DE
dc.rights.nationallicensefalsede_DE
item.creatorOrcidBessa, Wallace Moreira-
item.creatorOrcidBrinkmann, Gerrit-
item.creatorOrcidDücker, Daniel-André-
item.creatorOrcidKreuzer, Edwin-
item.creatorOrcidSolowjow, Eugen-
item.creatorGNDBessa, Wallace Moreira-
item.creatorGNDBrinkmann, Gerrit-
item.creatorGNDDücker, Daniel-André-
item.creatorGNDKreuzer, Edwin-
item.creatorGNDSolowjow, Eugen-
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptMechanik und Meerestechnik M-13-
crisitem.author.deptMechanik und Meerestechnik M-13-
crisitem.author.deptMechanik und Meerestechnik M-13-
crisitem.author.deptMechanik und Meerestechnik M-13-
crisitem.author.orcid0000-0002-0935-7730-
crisitem.author.orcid0000-0001-7256-6984-
crisitem.author.orcid0000-0001-5222-3706-
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