2022
DOI: 10.1088/1742-6596/2258/1/012063
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Design of interval type-2 fuzzy fractional order PID controller based on particle swarm optimization

Abstract: In this paper, an interval type-2 fuzzy fractional order PID controller (IT2F-FOPIDC) based on particle swarm optimization is proposed. The proposed controller has two adjustable degree of freedom parameters, so the control range of the controller parameters becomes larger and can control the controlled object more flexibly. Aiming at the difficult problem of many controller parameters and tuning, this paper introduces particle swarm optimization algorithm to optimize the controller parameters. In order to avo… Show more

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“…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. The fundamental issue, however, is the complexity of designing and constructing interval type-2 fuzzy controllers, which contain more parameters than their type-1 counterparts; therefore, this causes greater computational complexity and overhead issues [88][89][90][91][92][93][94][95][96][97][98][99]. Therefore, several efforts were made to reduce the complexity of generalized interval type-2 fuzzy logic systems; for example, Samui and Samarjit [100] published a neural network (NN)based tuning mechanism and Cagri and Tufan [101] developed a differential flatness-based controller, which both enable computation with generalized type-2 FLS (GT2FLS).…”
Section: Number Of Output Fuzzy Membership Functionsmentioning
confidence: 99%
“…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. The fundamental issue, however, is the complexity of designing and constructing interval type-2 fuzzy controllers, which contain more parameters than their type-1 counterparts; therefore, this causes greater computational complexity and overhead issues [88][89][90][91][92][93][94][95][96][97][98][99]. Therefore, several efforts were made to reduce the complexity of generalized interval type-2 fuzzy logic systems; for example, Samui and Samarjit [100] published a neural network (NN)based tuning mechanism and Cagri and Tufan [101] developed a differential flatness-based controller, which both enable computation with generalized type-2 FLS (GT2FLS).…”
Section: Number Of Output Fuzzy Membership Functionsmentioning
confidence: 99%