[-] Show simple item record

dc.contributor.advisorMcLaren, Robert W.eng
dc.contributor.authorKavirayani, Srikantheng
dc.date.issued2005eng
dc.date.submitted2005 Falleng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file viewed on (January 23, 2007)eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionThesis (M.S.) University of Missouri-Columbia 2005.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.eng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] A two joint inverted pendulum control system is an example of a highly nonlinear dynamic system which is investigated in this thesis in terms of control and system identification. These dual terms are very important in the area of adaptive automatic control in which parameter changes have a crucial effect on the performance of the controlled plant. The plant to be controlled consists of two pendulums that are to be maintained close to their vertical unstable equilibrium positions by applying a force on the cart. This thesis investigates classical optimal control techniques such as linear quadratic control applied for controlling the linearized and the nonlinear models of this two joint pendulum control system. It also investigates parameter estimation techniques using optimal Kalman filters. System identification, is often identified as an important step in the design and analysis of controllers for both linear and nonlinear plants. Therefore, linear model prediction techniques such as Box Jenkins and nonlinear approximation using neural networks are also investigated. The application of neural networks for system identification has provided interesting and useful results, which demonstrates the benefits of neural networks in application to control, and based on the results obtained in this investigation, the limitations in the use of neural networks to adaptive control have been observed.eng
dc.identifier.merlinb57675491eng
dc.identifier.urihttp://hdl.handle.net/10355/5835
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2005 UM restricted theses (MU)eng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess is limited to the campuses of the University of Missouri.eng
dc.subject.lcshAutomatic controleng
dc.subject.lcshPendulum -- Design and constructioneng
dc.subject.lcshNeural networks (Computer science)eng
dc.titleClassical and neural net control and identification of non-linear systems with application to the two-joint inverted pendulum control problemeng
dc.typeThesiseng
thesis.degree.disciplineElectrical engineering (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


Files in this item

[PDF]
[PDF]
[PDF]

This item appears in the following Collection(s)

[-] Show simple item record