2017
DOI: 10.1108/ijicc-07-2016-0026
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Two fuzzy internal model control methods for nonlinear uncertain systems

Abstract: Purpose The purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties. Design/methodology/approach The dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. I… Show more

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“…This approach is very interesting to approximate nonlinear system by extending linear theory concept to nonlinear system and using type-1 fuzzy sets (T1 FSs). Many works devoted to T1 TS fuzzy model have been developed dealing with stability and stabilization (Tanaka and Wang, 2001;Feng, 2006), observer design (Ma et al, 1998;Bergsten et al, 2002), state estimation (Gao et al, 2008;, diagnosis (Patton et al, 1998;Marx et al, 2007) and control (Aydi et al, 2017;Chang et al, 2009). Recently, in 1998, an interval type-2 fuzzy logic system (IT2 FLS) was proposed by Mendel and Karnik to deal with nonlinear plants subject to uncertainties.…”
Section: Introductionmentioning
confidence: 99%
“…This approach is very interesting to approximate nonlinear system by extending linear theory concept to nonlinear system and using type-1 fuzzy sets (T1 FSs). Many works devoted to T1 TS fuzzy model have been developed dealing with stability and stabilization (Tanaka and Wang, 2001;Feng, 2006), observer design (Ma et al, 1998;Bergsten et al, 2002), state estimation (Gao et al, 2008;, diagnosis (Patton et al, 1998;Marx et al, 2007) and control (Aydi et al, 2017;Chang et al, 2009). Recently, in 1998, an interval type-2 fuzzy logic system (IT2 FLS) was proposed by Mendel and Karnik to deal with nonlinear plants subject to uncertainties.…”
Section: Introductionmentioning
confidence: 99%