2021
DOI: 10.1109/access.2021.3114311
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Adaptive Interval Type-2 Fuzzy Tracking Control of PV Grid-Connected Inverters

Abstract: Fuzzy logic systems with approximation capabilities provide effective control for nonlinear and uncertain systems. Due to the characteristics of photovoltaic (PV) and the PWM method, a grid-connected PV system is a considerably nonlinear system with unpredictable parameters. In this study, a new adaptive interval type-2 fuzzy approximation-based controller (AIT2FAC) was developed to control a three-phase grid-connected PV system. The proposed controller can be implemented without any prior knowledge of the sys… Show more

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Cited by 7 publications
(3 citation statements)
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“…Furthermore, in the last three years of studies on higher-order types of FLS in particular, the designed and developed applications of interval type-2 fuzzy logic have increased significantly [48][49][50][51][52][53][54]. These type-2-based FLS applications have been identified in artificial intelligence (AI) [55][56][57][58][59], adaptive control [60][61][62][63][64][65][66], electric motor control [67][68][69][70][71][72], Internet of Things (IoT) [73][74][75][76][77], digital image processing [78][79][80][81][82][83][84] and other areas [85][86][87]. Of course, the application of interval type-2 fuzzy logic in the domain of control has recently attracted a lot of attention due to its better performance under uncertain conditions.…”
Section: Number Of Output Fuzzy Membership Functionsmentioning
confidence: 99%
“…Furthermore, in the last three years of studies on higher-order types of FLS in particular, the designed and developed applications of interval type-2 fuzzy logic have increased significantly [48][49][50][51][52][53][54]. These type-2-based FLS applications have been identified in artificial intelligence (AI) [55][56][57][58][59], adaptive control [60][61][62][63][64][65][66], electric motor control [67][68][69][70][71][72], Internet of Things (IoT) [73][74][75][76][77], digital image processing [78][79][80][81][82][83][84] and other areas [85][86][87]. Of course, the application of interval type-2 fuzzy logic in the domain of control has recently attracted a lot of attention due to its better performance under uncertain conditions.…”
Section: Number Of Output Fuzzy Membership Functionsmentioning
confidence: 99%
“…At each period T, individual currents iN and iP are compared to the sum of all currents representing the firing intervals, and the results are intercalated at the output, with an inverted signal for the N portion of the signal. Combining (10)-( 12) results in (13).…”
Section: ( ) ( ) ( )mentioning
confidence: 99%
“…Previous studies demonstrated the potential of this kind of controller, presenting greater robustness to uncertainty [13] and being capable of representing systems with a smaller number of membership functions and rules when compared with an equivalent type-1 fuzzy inference system [12]. This is especially important to reduce total power in an analog implementation, given that fewer membership functions and rules mean fewer hardware components required to generate those functions [14].…”
Section: Introductionmentioning
confidence: 99%