Parameter Estimation of PID Controller Using Machine Learning

Tanuja Pathare, Masud Akatar, Abdul Mateen Abdul Hai Momin, Rajeev Ranjan Pathak, Leah S Joshi, Pooja Chandaragi

2025

Abstract

DL (Deep Learning) method of approach towards PID (Proportional-Integral-Derivative) parameter tuning is inspired by the improvisation of Ziegler-Nichols method and linear regression. For any varying values of characteristics of a PID controller i.e, Ess (Steady State Error), peak overshoot, settling time, and rise time; a unique solution is obtained for kp, ki, and kd. This is demonstrated by the means of a more efficient method which is DL. Research is proposed to acknowledge which of the three mentioned methods provides the best fit for a model.Using the older methods for PID parameter tuning can be proven to slower the rate of process or cause human error. Hence, to avoid this an advanced tuning method is proposed via machine learning

Paper Citation

in Harvard Style

Pathare T., Akatar M., Momin A., Pathak R., Joshi L. and Chandaragi P. (2025). Parameter Estimation of PID Controller Using Machine Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 387-392. DOI: 10.5220/0013617700004664

in Bibtex Style

@conference{incoft25,
author={Tanuja Pathare and Masud Akatar and Abdul Mateen Abdul Hai Momin and Rajeev Ranjan Pathak and Leah Joshi and Pooja Chandaragi},
title={Parameter Estimation of PID Controller Using Machine Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={387-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013617700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Parameter Estimation of PID Controller Using Machine Learning
SN - 978-989-758-763-4
AU - Pathare T.
AU - Akatar M.
AU - Momin A.
AU - Pathak R.
AU - Joshi L.
AU - Chandaragi P.
PY - 2025
SP - 387
EP - 392
DO - 10.5220/0013617700004664
PB - SciTePress