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PID Tuning Method Using Single-Valued Neutrosophic Cosine Measure and Genetic Algorithm

Jun Ye

Department of Electrical and Information Engineering, Shaoxing University, 508 Huancheng West Road, Shaoxing, Zhejiang Province 312000, P.R. China

* Corresponding Authors: Jun Ye, email (Jun Ye), email

Intelligent Automation & Soft Computing 2019, 25(1), 15-23. https://doi.org/10.31209/2018.100000067

Abstract

Because existing proportional-integral-derivative (PID) tuning method using similarity measures of single-valued neutrosophic sets (SVNSs) and an increasing step algorithm shows its complexity and inconvenience, this paper proposes a PID tuning method using a cosine similarity measure of SVNSs and genetic algorithm (GA) to improve the existing PID tuning method. In the tuning process, the step response characteristic values (rising time, settling time, overshoot ratio, undershoot ratio, peak time, and steady-state error) of the control system are converted into the single-valued neutrosophic set (SVNS) by the neutrosophic membership functions (Neutrosophication). Then the values of three appropriate parameters in a PID controller can be determined by GA corresponding to the maximum similarity measure value between the actual (real) single-valued neutrosophic set (SVNS) and a previously determined ideal SVNS. Finally, the proposed method is tested on two actual examples, and then the simulation results show the effectiveness and convenience of the proposed PID tuning method.

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Cite This Article

APA Style
Ye, J. (2019). PID tuning method using single-valued neutrosophic cosine measure and genetic algorithm. Intelligent Automation & Soft Computing, 25(1), 15-23. https://doi.org/10.31209/2018.100000067
Vancouver Style
Ye J. PID tuning method using single-valued neutrosophic cosine measure and genetic algorithm. Intell Automat Soft Comput . 2019;25(1):15-23 https://doi.org/10.31209/2018.100000067
IEEE Style
J. Ye, "PID Tuning Method Using Single-Valued Neutrosophic Cosine Measure and Genetic Algorithm," Intell. Automat. Soft Comput. , vol. 25, no. 1, pp. 15-23. 2019. https://doi.org/10.31209/2018.100000067



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