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dc.contributor.authorLiut, Daniel Armandoen_US
dc.date.accessioned2014-03-14T20:17:00Z
dc.date.available2014-03-14T20:17:00Z
dc.date.issued1999-08-18en_US
dc.identifier.otheretd-100199-072001en_US
dc.identifier.urihttp://hdl.handle.net/10919/29163
dc.description.abstractNeural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systems

Daniel Armando Liut

ABSTRACT)

The goal of this thesis is to develop nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network configurations.

The first topic of this research is based on a multidisciplinary simulation, which includes ship-motion simulation by means of a numerical model called LAMP, the modeling of fins and computation of the hydrodynamic forces produced by them, and a neural-network/fuzzy-logic controller. LAMP is based on a source-panel method to model the flowfield around the ship, whereas the fins are modeled by a general unsteady vortex-lattice method. The ship is considered to be a rigid body and the complete equations of motion are integrated numerically in the time domain. The motion of the ship and the complete flowfield are calculated simultaneously and interactively. The neural-network/fuzzy-logic controller can be progressively trained.

The second topic is the development of a neural-network-based approach for the control of seismic structural response. To this end, a two-dimensional linear model and a hysteretic model of a multistory building are used. To control the response of the structure a tuned mass damper is located on the roof of the building. Such devices provide a good passive reduction. Once the mass damper is properly tuned, active control is added to improve the already efficient passive controller. This is achieved by means of a neural network.

As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural-network and fuzzy-logic controllers for both naval and civil engineering applications. The first strategy is based on a load-matching procedure, which seeks to adjust the controller in order to counteract the loads (forces and moments) which generate the motion that is to be reduced. A second training strategy provides training by means of an adaptive gradient search. This technique provides a wide flexibility in defining the parameters to be optimized. Also a novel neural-network approach called modal neural network is designed as a suitable controller for multiple-input multiple output control systems (MIMO).

en_US
dc.publisherVirginia Techen_US
dc.relation.haspartliut_phd_00.pdfen_US
dc.relation.haspartliut_phd_01.pdfen_US
dc.relation.haspartliut_phd_02.pdfen_US
dc.relation.haspartliut_phd_03.pdfen_US
dc.relation.haspartliut_phd_04.pdfen_US
dc.relation.haspartliut_phd_06.pdfen_US
dc.relation.haspartliut_phd_07.pdfen_US
dc.relation.haspartliut_phd_05.pdfen_US
dc.relation.haspartliut_phd_08.pdfen_US
dc.relation.haspartliut_phd_09.pdfen_US
dc.relation.haspartliut_phd_10.pdfen_US
dc.relation.haspartliut_phd_bi.pdfen_US
dc.relation.haspartliut_phd_vi.pdfen_US
dc.rightsI hereby grant to Virginia Tech or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University Libraries in all forms of media, now or hereafter known. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation.en_US
dc.subjectload-matching procedureen_US
dc.subjectship-motion control with finsen_US
dc.subjectlinear and hysteretic building structuresen_US
dc.subjectunsteady source-vortex-lattice hydrodynamicsen_US
dc.subjectmodal neural networks.en_US
dc.subjectadaptive gradient searchen_US
dc.subjecttuned mass dampersen_US
dc.subjectneural-network and fuzzy-logic controlen_US
dc.titleNeural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systemsen_US
dc.typeDissertationen_US
dc.contributor.departmentEngineering Mechanicsen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineEngineering Mechanicsen_US
dc.contributor.committeechairMook, Dean T.en_US
dc.contributor.committeememberVanLandingham, Hugh F.en_US
dc.contributor.committeememberHughes, Owen F.en_US
dc.contributor.committeememberRagab, Saad A.en_US
dc.contributor.committeememberMatheu, Enrique E.en_US
dc.contributor.committeememberNayfeh, Ali H.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-100199-072001/en_US
dc.date.sdate1999-10-01en_US
dc.date.rdate2000-10-05
dc.date.adate1999-10-05en_US


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