Fuzzy model predictive control of non-linear processes using genetic algorithms

Sarimveis, H; Bafas, G

dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Bafas, G	en
dc.date.accessioned	2014-03-01T01:19:00Z
dc.date.available	2014-03-01T01:19:00Z
dc.date.issued	2003	en
dc.identifier.issn	0165-0114	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15317
dc.subject	Fuzzy control	en
dc.subject	Fuzzy models	en
dc.subject	Genetic algorithms	en
dc.subject	Model predictive control	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	Dynamic programming	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Model predictive control	en
dc.subject.other	Fuzzy control	en
dc.title	Fuzzy model predictive control of non-linear processes using genetic algorithms	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0165-0114(02)00506-7	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0165-0114(02)00506-7	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	This paper introduces a new fuzzy control technique, which belongs to the popular family of control algorithms, called Model Predictive Controllers. The method is based on a dynamic fuzzy model of the process to be controlled, which is used for predicting the future behavior of the output variables. A non-linear optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over the prediction horizon and the control energy over a shorter control horizon. The problem is solved on line using a specially designed genetic algorithm, which has a number of advantages over conventional non-linear optimization techniques. The method can be used with any type of fuzzy model and is particularly useful when a direct fuzzy controller cannot be designed due to the complexity of the process and the difficulty in developing fuzzy control rules. The method is illustrated via the application to a non-linear single-input single-output reactor, where a Takagi-Sugeno model serves as a predictor of the process future behavior. (C) 2002 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Fuzzy Sets and Systems	en
dc.identifier.doi	10.1016/S0165-0114(02)00506-7	en
dc.identifier.isi	ISI:000185307400003	en
dc.identifier.volume	139	en
dc.identifier.issue	1	en
dc.identifier.spage	59	en
dc.identifier.epage	80	en