Bir Fırçalı Redüktörlü Dc Motorda Yapay Zeka Yöntemleriyle Tork

Year 2022, , 885 - 898, 30.12.2022

Serkan Beller

https://doi.org/10.21605/cukurovaumfd.1230790

Abstract

Günümüzde elektrik-elektronikteki ilerlemelerle birlikte DC motorların kullanım alanları oldukça artmıştır. DC motorlar yüksek başlangıç torklarına sahiptir ve hızları geniş bir aralıkta ayarlanabilir. Mevcut deneysel çalışmada motor miline bağlı olan farklı ağırlıklar, farklı hızlarda, değişken uzaklıklarda, 0º-345º derece açı aralığında döndürülmüştür. Böylece DC motorun ürettiği farklı tork değerleri gözlemlenmiştir. Bazı durumlarda düşük dönme hızlarında üretilen tork miktarı doğrusal olmayan değerlere sahip olabilmektedir. Bu durum doğru tork tahmini için yapay zeka metotlarının kullanılmasına imkan sağlamaktadır. Mevcut çalışmada en iyi tork değerlerinin tahmini için Elman Backpropagation Neural Network (EBNN) ve General Regression Neural Network (GRNN) ağlarının farklı kullanımlarına yer verilmiştir. Performans kıyaslamaları ortalama karesel hata (MSE), regresyon katsayısı (R2), kök karesel hata (RSE), ve ortalama mutlak hata (MAE) değerlerine göre yapılmıştır.

Keywords

DC motor, Tork tahmini, Yapay zeka, EBNN, GRNN

Torque Estimation with Artificial Intelligence Methods in a Brushed Geared Dc Motor

Abstract

Today, with the advances in electricity-electronics, the usage areas of DC motors have increased considerably. DC motors have high starting torques and speed can be adjusted over a wide range. In the present experimental study, different weights connected to the motor shaft were rotated at different speeds, at variable distances, in the angle range of 0º-345º degrees. Thus, different torque values produced by the DC motor were observed. In some cases, the amount of torque produced at low rotational speeds may have non-linear values. This allows the use of artificial intelligence methods for accurate torque estimation. In the present study, different uses of Elman Backpropagation Neural Network (EBNN) and General Regression Neural Network (GRNN) are given for the estimation of the best torque values. Performance comparisons were made according to mean square error (MSE), regression coefficient (R2), root square error (RSE), and mean absolute error (MAE) values.

Keywords

DC motor, Torque estimation, Artificial intelligence, EBNN, GRNN

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