Design of a model identification fuzzy adaptive controller and stability analysis of nonlinear processes

Sagias, DI; Sarafis, EN; Siettos, CI; Bafas, GV

dc.contributor.author	Sagias, DI	en
dc.contributor.author	Sarafis, EN	en
dc.contributor.author	Siettos, CI	en
dc.contributor.author	Bafas, GV	en
dc.date.accessioned	2014-03-01T01:16:17Z
dc.date.available	2014-03-01T01:16:17Z
dc.date.issued	2001	en
dc.identifier.issn	0165-0114	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14015
dc.subject	Limit points	en
dc.subject	Model identification fuzzy adaptive controller	en
dc.subject	Nonlinear system	en
dc.subject	Stability analysis	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Mathematics, Applied	en
dc.subject.classification	Statistics & Probability	en
dc.subject.other	Adaptive control systems	en
dc.subject.other	Control system analysis	en
dc.subject.other	Identification (control systems)	en
dc.subject.other	Mathematical models	en
dc.subject.other	Nonlinear control systems	en
dc.subject.other	Robustness (control systems)	en
dc.subject.other	System stability	en
dc.subject.other	Limit points	en
dc.subject.other	Self tuning controllers	en
dc.subject.other	Fuzzy control	en
dc.title	Design of a model identification fuzzy adaptive controller and stability analysis of nonlinear processes	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0165-0114(99)00153-0	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0165-0114(99)00153-0	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	This paper deals with the design of a model identification fuzzy adaptive controller with real-time scaling factors adjustment and the stability analysis of nonlinear distributed parameter systems. The solution branch of such systems frequently contains limit points (or turning points) which represent the boundary between stability and instability of the system. Hence, stability analysis is required for the determination of the stable and unstable operating regions. The performance of the proposed fuzzy self-tuning controller is compared to an equivalent conventional adaptive controller, over a wide range of step disturbances and operating regions. The proposed fuzzy adaptive scheme in comparison with the conventional adaptive scheme exhibits a much robust response, shorter settling times, overshooting less the controlled variable and smaller IAE of the manipulated variable for the entire range of step disturbances. (C) 2001 Elsevier Science 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(99)00153-0	en
dc.identifier.isi	ISI:000168762100013	en
dc.identifier.volume	121	en
dc.identifier.issue	1	en
dc.identifier.spage	169	en
dc.identifier.epage	179	en