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Artificial Intelligence Based PID Controller for an Eddy Current Dynamometer

İhsan Uluocak1,*, Hakan Yavuz2

1 Ceyhan Engineering Faculty, Mechanical Engineering Department, Çukurova University, Adana, Turkey
2 Faculty of Engineering, Mechanical Engineering Department, Çukurova University, Adana, Turkey

* Corresponding Author: İhsan Uluocak. Email:

Intelligent Automation & Soft Computing 2022, 33(2), 1229-1243.


This paper presents a design and real-time application of an efficient Artificial Intelligence (AI) method assembled with PID controller of an eddy current dynamometer (ECD) for robustness due to highly nonlinear system by reason of some magnetism phenomena such as skin effect and dissipated heat of eddy currents. PID Control which is known as the most popular conventional control method in industry is inadequate for such nonlinear systems. On the other hand, Adaptive Neural Fuzzy Interference System (ANFIS), Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), and Radial Basis Neural Network (RBNN) are examples used as artificial intelligence-based techniques that can increase the performance of conventional control systems in particular. The proposed control system proves changeable Kp (Proportional gain), Ki, (Integral gain) and Kd (Derivative gain) parameters in real-time to adapt and presents a good capacity to adapt nonlinearities and bring robustness using 4 different versatile soft computing methods of ANFIS, SHLNN, GRNN, and RBNN. The testing dataset is extracted from experimental studies and its robustness has also been verified with different Artificial Intelligence (AI) methods. The presented technique is observed to have a good performance in terms of response time (t) and accuracy of desired speed value (V) under different parameters such as non-linear dynamics (V, T) of the system elements and the varying load effects.


Cite This Article

. Uluocak and H. Yavuz, "Artificial intelligence based pid controller for an eddy current dynamometer," Intelligent Automation & Soft Computing, vol. 33, no.2, pp. 1229–1243, 2022.

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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