2016
DOI: 10.1177/1077546316653040
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Development of a multi-level adaptive fuzzy controller for beyond pull-in stabilization of electrostatically actuated microplates

Abstract: The objective of this paper is to present a supervised multi-level fuzzy controller to control the deflection of an electrostatically actuated microplate within and beyond its pull-in range. The mode shapes of the microplate are derived using Extended Kantorovich Method (EKM) which are shown to be in great agreement with finite element results. Using open loop simulations, it is shown that the first mode shape is effectively the dominant one. Then by utilizing a single mode approximation along with employing t… Show more

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Cited by 41 publications
(9 citation statements)
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References 32 publications
(44 reference statements)
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“…Fuzzy logic control (FLC) is a widely-used method to perform MPPT for the PV system. Numerous studies [17][18][19][20][21][22][23][24] in the existing literature have demonstrated that Type-1 FLC (T1FLC) does not succeed in highly uncertain situations in the system, while a Type-2 FLC using Type-2 fuzzy set display better performance. In the present paper, a hybrid control structure called AIC-IT2-TSK-FLC was developed for MPPT of the PV system with a combination of the AIC MPPT algorithm and Type-2 FLC [25].…”
Section: Proposed Hybrid Control Structurementioning
confidence: 99%
“…Fuzzy logic control (FLC) is a widely-used method to perform MPPT for the PV system. Numerous studies [17][18][19][20][21][22][23][24] in the existing literature have demonstrated that Type-1 FLC (T1FLC) does not succeed in highly uncertain situations in the system, while a Type-2 FLC using Type-2 fuzzy set display better performance. In the present paper, a hybrid control structure called AIC-IT2-TSK-FLC was developed for MPPT of the PV system with a combination of the AIC MPPT algorithm and Type-2 FLC [25].…”
Section: Proposed Hybrid Control Structurementioning
confidence: 99%
“…It has been proved that T-S fuzzy model can serve as a technically efficient tool to approximate smooth nonlinear systems to arbitrary degrees of accuracy within any convex compact set [16]. As a consequence, plenty of efforts have been devoted to T-S fuzzy systems aiming at industrial applications [17], [18] and theoretical study [19], [20].…”
Section: Introductionmentioning
confidence: 99%
“…Based on the conducted research works, nonlinearities play an important role in the failure of dynamic systems (Alsaleem and Younis, 2010). Hence, studying the control behaviors and enhancing the efficiency, accuracy, and stability of electrostatic MEMS have been a matter of interest of many researchers (Gu and Zhu, 2014; Radgolchin and Moeenfard, 2018). Several control methods such as sliding mode control (SMC) (Dastaviz and Binazadeh, 2019; Vahidi-Moghaddam et al, 2018), fuzzy control (Tooranjipour et al, 2019; Zou et al, 2019), and backstepping control (Meng et al, 2019) have been vastly used to control the nonlinear mechanical systems.…”
Section: Introductionmentioning
confidence: 99%