dc.contributor.author	Liut, Daniel Armando	en
dc.contributor.committeechair	Mook, Dean T.	en
dc.contributor.committeemember	VanLandingham, Hugh F.	en
dc.contributor.committeemember	Hughes, Owen F.	en
dc.contributor.committeemember	Ragab, Saad A.	en
dc.contributor.committeemember	Matheu, Enrique E.	en
dc.contributor.committeemember	Nayfeh, Ali H.	en
dc.contributor.department	Engineering Mechanics	en
dc.date.accessioned	2014-03-14T20:17:00Z	en
dc.date.adate	1999-10-05	en
dc.date.available	2014-03-14T20:17:00Z	en
dc.date.issued	1999-08-18	en
dc.date.rdate	2000-10-05	en
dc.date.sdate	1999-10-01	en
dc.description.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
dc.description.degree	Ph. D.	en
dc.identifier.other	etd-100199-072001	en
dc.identifier.sourceurl	http://scholar.lib.vt.edu/theses/available/etd-100199-072001/	en
dc.identifier.uri	http://hdl.handle.net/10919/29163	en
dc.publisher	Virginia Tech	en
dc.relation.haspart	liut_phd_00.pdf	en
dc.relation.haspart	liut_phd_01.pdf	en
dc.relation.haspart	liut_phd_02.pdf	en
dc.relation.haspart	liut_phd_03.pdf	en
dc.relation.haspart	liut_phd_04.pdf	en
dc.relation.haspart	liut_phd_06.pdf	en
dc.relation.haspart	liut_phd_07.pdf	en
dc.relation.haspart	liut_phd_05.pdf	en
dc.relation.haspart	liut_phd_08.pdf	en
dc.relation.haspart	liut_phd_09.pdf	en
dc.relation.haspart	liut_phd_10.pdf	en
dc.relation.haspart	liut_phd_bi.pdf	en
dc.relation.haspart	liut_phd_vi.pdf	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	load-matching procedure	en
dc.subject	ship-motion control with fins	en
dc.subject	linear and hysteretic building structures	en
dc.subject	unsteady source-vortex-lattice hydrodynamics	en
dc.subject	modal neural networks.	en
dc.subject	adaptive gradient search	en
dc.subject	tuned mass dampers	en
dc.subject	neural-network and fuzzy-logic control	en
dc.title	Neural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systems	en
dc.type	Dissertation	en
thesis.degree.discipline	Engineering Mechanics	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Ph. D.	en

Neural-Network and Fuzzy-Logic Learning and Control of Linear and Nonlinear Dynamic Systems

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