Optimum Setting of PID Controller using Particle Swarm Optimization for a Position Control System

Authors

  • Ahmed Khalaf Hamoudi Department of Control & Systems Engineering – University of Technology Baghdad - Iraq

Keywords:

Position of a DC motor, PID controller, Particle Swarm Optimization, Ziegler and Nichols’ method, performance indices

Abstract

The goal of this paper is to present a study of tuning the Proportional–Integral-Derivative (PID) controller for control the position of a DC motor by using the Particle Swarm Optimization (PSO) technique as well as the Ziegler & Nichols (ZN) technique. The conventional Ziegler & Nichols (ZN) method for tuning the PID controller gives a big overshoot and large settling time, so for this reason a modern control approach such as particle swarm optimization (PSO) is used to overcome this disadvantage. In this work, a third order system is considered to be the model of a DC motor. Four types of performance indices are used when using the particle swarm optimization technique. These indices are ISE, IAE, ITAE and ITSE. Also study the effect of each one of these performance indices by obtaining the percentage overshoot and settling time when a unit step input is applied to a DC motor. A comparison is made between the two methods for tuning the parameters of PID controller for control the position of a DC motor is considered. The first one is tuning the controller by using the Particle Swarm Optimization technique where the second is tuning by using the Ziegler & Nichols method. The proposed PID parameters adjustment by the Particle Swarm Optimization technique showed better results than the Ziegler & Nichols’ method. The obtained simulation results showed good validity of the proposed method. MATLAB programming and Simulink were adopted in this work.

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Published

01-01-2017

How to Cite

[1]
A. K. Hamoudi, “Optimum Setting of PID Controller using Particle Swarm Optimization for a Position Control System”, NJES, vol. 20, no. 1, pp. 292–297, Jan. 2017, Accessed: Nov. 27, 2024. [Online]. Available: https://nahje.com/index.php/main/article/view/105

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