2015
DOI: 10.1109/tfuzz.2014.2346235
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Indirect Adaptive Type-2 Fuzzy Impulsive Control of Nonlinear Systems

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Cited by 16 publications
(16 citation statements)
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“…It is found that impulsive control is very effective in a wide variety of applications for performance improvement of control process [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Ayati and Khaloozadeh [19] study the adaptive impulsive control to design an observer for nonlinear continuous systems.…”
Section: Introductionmentioning
confidence: 99%
“…It is found that impulsive control is very effective in a wide variety of applications for performance improvement of control process [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Ayati and Khaloozadeh [19] study the adaptive impulsive control to design an observer for nonlinear continuous systems.…”
Section: Introductionmentioning
confidence: 99%
“…However, the membership functions of the antecedent parts of fuzzy rules are crisp and should be selected by the expert. Using adaptive type-2 fuzzy/neural systems some useful and effective controllers have been proposed, for example [26][27][28][29][30][31][32][33]. The general idea for these methods is to set the consequent parameters of fuzzy rules free and tune them by adaptive laws derived via Lyapunov synthesis approach.…”
Section: Introductionmentioning
confidence: 99%
“…indirect adaptive control methods ([17]- [22]). In [17], [18], the authors proposed a stable control scheme for T-S Models 85 (i.e.…”
mentioning
confidence: 99%
“…These multiple adaptive T-S identification models form the basis for deriving the corresponding fuzzy controllers which are designed to control a class of dynamical fuzzy systems. The aforementioned control schemes appearing in [17]-[20], [22]- [24] are designed to be very effective when there is not any modeling error, that is, when the fuzzy 100 model describes the plant accurately. Although, it has been experimentally shown that they perform satisfactorily well when they are applied in the real plant ( [17], [24]), due to modeling errors, the stability analysis which is made only for the fuzzy model, cannot guarantee that the real nonlinear plant will remain stable.…”
mentioning
confidence: 99%
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