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Tuning Rules for Fractional Order PID Controller Using Data Analytics

P. R. Varshini*, S. Baskar, M. Varatharajan, S. Sadhana

Department of EEE, Thiagarajar College of Engineering, Madurai, Tamilnadu, 625015, India

* Corresponding Author: P. R. Varshini. Email: email

Intelligent Automation & Soft Computing 2022, 33(3), 1787-1799. https://doi.org/10.32604/iasc.2022.024192

Abstract

Flexibility and robust performance have made the FOPID (Fractional Order PID) controllers a better choice than PID (Proportional, Integral, Derivative) controllers. But the number of tuning parameters decreases the usage of FOPID controllers. Using synthetic data in available FOPID tuners leads to abnormal controller performances limiting their applicability. Hence, a new tuning methodology involving real-time data and overcomes the drawbacks of mathematical modeling is the need of the hour. This paper proposes a novel FOPID controller tuning methodology using machine learning algorithms. Feed Forward Back Propagation Neural Network (FFBPNN), Multi Least Squares Support Vector Regression (MLSSVR) chosen to design Machine Learning based Optimal Tuner (MLOT) can handle the interdependency between the controller parameters and multiple outputs for multiple inputs.The proposed tuner finds application in the control of power and energy systems. It can accomplish tracking, disturbance-rejection, and robustness controller performances, thus making FOPID controller design easier and accurate. Comparisons with existing FOPID tuning rules show better controller performances and easy tuning. Thus, this paper addresses a unique, real-time, model-free, easily tunable FOPID tuning methodology satisfying plant requirements.


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APA Style
Varshini, P.R., Baskar, S., Varatharajan, M., Sadhana, S. (2022). Tuning rules for fractional order PID controller using data analytics. Intelligent Automation & Soft Computing, 33(3), 1787-1799. https://doi.org/10.32604/iasc.2022.024192
Vancouver Style
Varshini PR, Baskar S, Varatharajan M, Sadhana S. Tuning rules for fractional order PID controller using data analytics. Intell Automat Soft Comput . 2022;33(3):1787-1799 https://doi.org/10.32604/iasc.2022.024192
IEEE Style
P.R. Varshini, S. Baskar, M. Varatharajan, and S. Sadhana "Tuning Rules for Fractional Order PID Controller Using Data Analytics," Intell. Automat. Soft Comput. , vol. 33, no. 3, pp. 1787-1799. 2022. https://doi.org/10.32604/iasc.2022.024192



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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