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Differential Evolution Based PID Antenna Position Control System
Subject area: Science,Engineering and Technology · Area of research: Control System
Abstract
This paper presents a robust and efficient way of tuning PID controller using different variants of differential evolution (DE) algorithms for antenna positioning system. 20 DE variants were implemented out of which DE/rand/1/bin appear to be more promising in addressing this problem with peak overshot of 0.0052, rise time of 0.01sec, and settling time of 0.01sec. This has an overall cost or objective fitness function of 0.0162. The second best optimizer is DE/best/1/exp.
Keywords
Differential evolution algorithms, PID controller, Step response, Ziegler?Nichols tuning method, optimization, objective fitness function.
References
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[4] Katsuhiko Ogata Modern Control Engineering Fifth Edition Upper Saddle River 3010
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[6] S. Iliya. Application of Computational Intelligence inCognitive Radio Network for Efficient SpectrumUtilization, and Speech Therapy. PhD Thesis, 2017.
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How to cite this paper
@article{1704678,
author = {Sunday Iliya, Felix Egbujo, Olurotimi Olakunle Awodiji, Philip Omolaye},
title = {Differential Evolution Based PID Antenna Position Control System},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {12},
pages = {601-606},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704678.pdf},
abstract = {This paper presents a robust and efficient way of tuning PID controller using different variants of differential evolution (DE) algorithms for antenna positioning system. 20 DE variants were implemented out of which DE/rand/1/bin appear to be more promising in addressing this problem with peak overshot of 0.0052, rise time of 0.01sec, and settling time of 0.01sec. This has an overall cost or objective fitness function of 0.0162. The second best optimizer is DE/best/1/exp.},
keywords = {Differential evolution algorithms, PID controller, Step response, Ziegler?Nichols tuning method, optimization, objective fitness function.},
month = {June},
}