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Particle Swarm Intelligence Based PID Position Control System
Subject area: Science,Engineering and Technology · Area of research: Control System
Abstract
This paper present a robust and efficient way of tuning PID controller using three variants of swam intelligence algorithms for control of a positioning system. Out of the three variants implemented, toroidal bound comprehensive learning particle swarm optimization (CLPSO) appear to be more promising in addressing this problem with peak overshot of 0.0176, rise tie of 0.01s, setting time of 0.01s and combined cost function of 0.0134 followed by toroidal bound inertia PSO. The results obtained using the swarm intelligence algorithm variants outperform those of Deferential Evolution (DE) variants used in solving the similar problem as presented in [7].
Keywords
Swarm intelligent algorithms, PID controller, Step response, Ziegler?Nichols tuning method, optimization, objective fitness function.
References
[1] A.R. Laware1, V.S. Banda and D.B. Talange. Real Time Temperature Control System Using PID Controller and Supervisory Control and Data Acquisition System (SCADA), International Journal of Application or Innovation in Engineering & Management, Volume 2, Issue 2, 2013
[2] I J Narath and M. Gopal: Control system Engineering, fourth Edition, 2006
[3] J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar. Comprehensive learning particleswarm optimizer for global optimization of multimodal functions. IEEE Transactionson Evolutionary Computation, 10(3):281–295, 2006.
[4] Katsuhiko Ogata Modern Control Engineering Fifth Edition Upper Saddle River 3010
[5] K Smriti Rao, Ravi Mishra Comparative study of P, PI and PID controller for speed control of VSI-fed induction motor, International Journal of Engineering Development and Research, Volume 2, Issue 2, 2014
[6] S. Iliya. Application of Computational Intelligence in Cognitive Radio Network for Efficient Spectrum Utilization, and Speech Therapy. PhD Thesis, 2017.
[7] S. Iliya. Differential Evolution Based PID Antenna Position Control System. International Journal of Scientific and Engineering Research, July 2017.
[8] Y. Shi and R. Eberhart. A modified particle swarm optimizer. In Proceedings of theIEEE Congress on Evolutionary Computation, 1998.
How to cite this paper
@article{1704680,
author = {Sunday Iliya, Timothy Afiagh, Olurotimi Olakunle Awodiji},
title = {Particle Swarm Intelligence Based PID Position Control System},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {12},
pages = {607-613},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1704680.pdf},
abstract = {This paper present a robust and efficient way of tuning PID controller using three variants of swam intelligence algorithms for control of a positioning system. Out of the three variants implemented, toroidal bound comprehensive learning particle swarm optimization (CLPSO) appear to be more promising in addressing this problem with peak overshot of 0.0176, rise tie of 0.01s, setting time of 0.01s and combined cost function of 0.0134 followed by toroidal bound inertia PSO. The results obtained using the swarm intelligence algorithm variants outperform those of Deferential Evolution (DE) variants used in solving the similar problem as presented in [7].},
keywords = {Swarm intelligent algorithms, PID controller, Step response, Ziegler?Nichols tuning method, optimization, objective fitness function.},
month = {June},
}