Open Access

ARTICLE

Model Predictive Control Coupled with Artificial Intelligence for Eddy Current Dynamometers

İhsan Uluocak^1,*, Hakan Yavuz²

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

* Corresponding Author: İhsan Uluocak. Email:

Computer Systems Science and Engineering 2023, 44(1), 221-234. https://doi.org/10.32604/csse.2023.025426

Received 23 November 2021; Accepted 24 December 2021; Issue published 01 June 2022

Abstract

The recent studies on Artificial Intelligence (AI) accompanied by enhanced computing capabilities supports increasing attention into traditional control methods coupled with AI learning methods in an attempt to bringing adaptiveness and fast responding features. The Model Predictive Control (MPC) technique is a widely used, safe and reliable control method based on constraints. On the other hand, the Eddy Current dynamometers are highly nonlinear braking systems whose performance parameters are related to many processes related variables. This study is based on an adaptive model predictive control that utilizes selected AI methods. The presented approach presents an updated the mathematical model of an Eddy Current Dynamometer based on experimentally obtained system operational data. Finally, the comparison of AI methods and related learning performances based on the assessment technique of mean absolute percentage error (MAPE) issues are discussed. The results indicate that Single Hidden Layer Neural Network (SHLNN), General Regression Neural Network (GRNN), Radial Basis Network (RBNN), Neuro Fuzzy Network (ANFIS) coupled MPC have quite satisfying performances. The presented results indicate that, amongst them, GRNN appears to provide the best performance.

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Keywords

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