@inproceedings{c492ec432d884ff2b81ebb8eacd0d055,
title = "Review on Determinant Parameters of Genetic Algorithm for the Optimized Closed Loop Control",
abstract = "This paper aims to analyze the determinant parameters of Genetic Algorithm (GA) analysis for the optimized control performance of a closed control loop. The determinant parameters of GA optimization analysis cover population size (nPop), mutation rate (mu) and iteration (iter.) are analyzed and justified. The control terminology covers the Proportional-Integral-Derivative (PID) controller, a prestigious solution for industrial control applications. Besides, the research proposed stability analysis to determine the upper and lower limit settings for the optimization analysis. The research has begun with model identification, stability analysis and is followed by determining the controller tunings. The performance indexes are applied to compare the response performance of GA with deterministic controller tunings. Analysis results and discussion shows that GA with proper determinant parameters' settings are performing better than other tuning methods in the closed loop control performance.",
keywords = "Genetic Algorithm, closed loop control, determinant parameters, performance indexes, stability analysis",
author = "Chew, {Ing Ming} and Wong, {W. K.} and Juwono, {Filbert H.} and Tiong, {Teck Chai}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022 ; Conference date: 26-10-2022 Through 28-10-2022",
year = "2022",
doi = "10.1109/GECOST55694.2022.10010678",
language = "English",
series = "2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "187--192",
booktitle = "2022 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2022",
}