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DC motor Analysis Based on Improvement of PID Coefficients Using PSO Algorithm for Educational Use

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Learning in the Age of Digital and Green Transition (ICL 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 633))

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Abstract

This paper explains mainly and plainly how to control the speed of a permanent magnet direct current (PMDC) motor with a PID control analysis at the educational level. While controlling, PMDC motor speed analysis has been made by changing the PID coefficients, parameters such as oscillation effect, maximum overshoot, rise time, and settling time have been examined in detail based. The most ideal state has been tried to be found by replacing PID coefficients. At the same time, PID tuning has been performed by using the particle swarm optimization (PSO) algorithm. PID coefficient correction has been performed in Matlab with some iterations and the subject has been examined from the perspective of comparing different combinations. In this way, it has been tried to contribute to the literature by observing the effect of the PSO algorithm on the PID tuning. It is thought that this study will guide the basics of automation and control projects in future studies on PMDC motor control characteristics.

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Authors and Affiliations

  1. Institute of Science, Electric-Electronic Engineering, Marmara University, Maltepe Campus, 34 722, Istanbul, Turkey

    Enes Kumru & Necibe Fusun Oyman Serteller

Authors
  1. Enes Kumru
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  2. Necibe Fusun Oyman Serteller
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Corresponding author

Correspondence to Enes Kumru .

Editor information

Editors and Affiliations

  1. CTI Global, Frankfurt, Germany

    Michael E. Auer

  2. Federal Ministry of Education, Science and Research, Vienna, Austria

    Wolfgang Pachatz

  3. Tallinn University of Technology, Tallinn, Estonia

    Tiia Rüütmann

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Kumru, E., Serteller, N.F.O. (2023). DC motor Analysis Based on Improvement of PID Coefficients Using PSO Algorithm for Educational Use. In: Auer, M.E., Pachatz, W., Rüütmann, T. (eds) Learning in the Age of Digital and Green Transition. ICL 2022. Lecture Notes in Networks and Systems, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-031-26876-2_85

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