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dc.contributor.authorSmith, Bradley R.en_US
dc.date.accessioned2014-03-14T20:18:36Z
dc.date.available2014-03-14T20:18:36Z
dc.date.issued1997-12-02en_US
dc.identifier.otheretd-111597-81423en_US
dc.identifier.urihttp://hdl.handle.net/10919/29607
dc.description.abstractThe nonlinearities of a nonlinear system can degrade the performance of a closed-loop system. In order to improve the performance of the closed-loop system, an adaptive technique, using a neural network, was developed. A neural network is placed in series between the output of the fixed-gain controller and the input into the plant. The weights are initialized to values that result in a unity gain across the neural network, which is referred to as a "feed-through neural network." The initial unity gain causes the output of the neural network to be equal to the input of neural network at the beginning of the convergence process. The result is that the closed-loop system's performance with the neural network is, initially, equal to the closed-loop system's performance without the neural network. As the weights of the neural network converge, the performance of the system improves. However, the back propagation algorithm was developed to update the weights of the feed-forward neural network in the open loop. Although the back propagation algorithm converged the weights in the closed loop, it worked very slowly. Two new update algorithms were developed for converging the weights of the neural network inside the closed-loop. The first algorithm was developed to make the convergence process independent of the plants dynamics and to correct for the effects of the closed loop. The second algorithm does not eliminate the effects of the plant's dynamics, but still does correct for the effects of the closed loop. Both algorithms are effective in converging the weights much faster than the back propagation algorithm. All of the update algorithms have been shown to work effectively on stable and unstable nonlinear plants.en_US
dc.publisherVirginia Techen_US
dc.relation.hasparttitle.pdfen_US
dc.relation.haspartchap1.pdfen_US
dc.relation.haspartchap2.pdfen_US
dc.relation.haspartchap3.pdfen_US
dc.relation.haspartchap4.pdfen_US
dc.relation.haspartchap5.pdfen_US
dc.relation.haspartchap6.pdfen_US
dc.relation.haspartchap7.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.subjectLMSen_US
dc.subjectadaptiveen_US
dc.subjectneural networksen_US
dc.subjectcontrolsen_US
dc.subjectill-modeleden_US
dc.titleNeural Network Enhancement of Closed-Loop Controllers for Ill-Modeled Systems with Unknown Nonlinearitiesen_US
dc.typeDissertationen_US
dc.contributor.departmentMechanical Engineeringen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairRobertshaw, Harry H.en_US
dc.contributor.committeememberVanLandingham, Hugh F.en_US
dc.contributor.committeememberSaunders, William R.en_US
dc.contributor.committeememberCudney, Harley H.en_US
dc.contributor.committeememberBaumann, William T.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-111597-81423/en_US
dc.date.sdate1997-12-02en_US
dc.date.rdate1997-12-15
dc.date.adate1997-12-15en_US


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