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Intelligent Process Control

dc.contributor.advisorBaras, J.S.en_US
dc.contributor.authorPatel, N.S.en_US
dc.date.accessioned2007-05-23T09:55:16Z
dc.date.available2007-05-23T09:55:16Z
dc.date.issued1993en_US
dc.identifier.urihttp://hdl.handle.net/1903/5450
dc.description.abstractIt is often observed that human experts can tune the parameters of a controller based on their knowledge and experience, rather than on complicated algorithms. In fact, more often than not, they have only a vague idea of the process model. An attempt is made here to create a fuzzy-logic based expert which would emulate such behavior. The expert is, specifically designed to tune the gains of a Proportional-Integral-Derivative (PID) controller, applied to stable dominant pole plants having large rise times. It is observed, that a number of plants found in the chemical process industry can be suitable modeled as such systems. a rule base for the expert was developed after analysis and simulation studies. Attempts have been made to keep the rules as few and simple as possible. At no point is any attempt made to estimate the parameters of the plant model. The expert observes only the output from the plant. Results of the application of the expert to a second order plant, to the separator temperature control loop of the Tennessee Eastman problem, and to a third order plant are presented. The expert is found to successfully tune the PID gains, and the results provide encouragement for the creation of such experts which can handle a class of plants.,en_US
dc.format.extent2154158 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; MS 1993-4en_US
dc.subjectartificial intelligenceen_US
dc.subjectchemical process controlen_US
dc.subjectfuzzy logicen_US
dc.subjectSystems Integrationen_US
dc.titleIntelligent Process Controlen_US
dc.typeThesisen_US
dc.contributor.departmentISRen_US


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