Particle Swarm Intelligence Based PID Position Control System

• Author(s): Sunday Iliya ; Timothy Afiagh ; Olurotimi Olakunle Awodiji
• Paper ID: 1704680
• Page: 607-613
• Published Date: 20-06-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 12 June-2023

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.

Citations

IRE Journals:
Sunday Iliya , Timothy Afiagh , Olurotimi Olakunle Awodiji "Particle Swarm Intelligence Based PID Position Control System" Iconic Research And Engineering Journals Volume 6 Issue 12 2023 Page 607-613

IEEE:
Sunday Iliya , Timothy Afiagh , Olurotimi Olakunle Awodiji "Particle Swarm Intelligence Based PID Position Control System" Iconic Research And Engineering Journals, 6(12)