Bir Fırçalı Redüktörlü Dc Motorda Yapay Zeka Yöntemleriyle Tork

Yıl 2022, , 885 - 898, 30.12.2022

Serkan Beller

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

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• ⦁ Direct Current Motor, https://www.science direct.com/topics/engineering/direct-current- motor, Access date: 19.05.2022.
• ⦁ Pololu Brushed DC Motor, https://www.pololu.com/product/3213, Access date: 03.05.2022.
• ⦁ Nouri, K., Dhaouadi, R., Braiek, N.B., 2008. Adaptive Control of a Nonlinear Dc Motor Drive Using Recurrent Neural Networks. Applied Soft Computing, 8, 371–382.
• ⦁ Yang, S.F., Chou, J.H., 2009. A Mechatronic Positioning System Actuated Using a Micro DC-Motor-Driven Propeller–Thruster. Mechatronics, 19, 912–926.
• ⦁ Reyes-Reyes, J., Astorga-Zaragoza, C.M., Adam-Medina, M., Guerrero-Ramı´rez, G.V., 2010. Bounded Neuro-Control Position Regulation for a Geared DC Motor. Engineering Applications of Artificial Intelligence, 23, 1398–1407.
• ⦁ Premkumar, K., Manikandan, B.V., 2014. Adaptive Neuro-Fuzzy Inference System based Speed Controller for Brushless DC Motor. Neurocomputing, 138, 260–270.
• ⦁ Ramadan, E.A., El-bardini, M., Fkirin, M.A., 2014. Design and FPGA-Implementation of an Improved Adaptive Fuzzy Logic Controller for DC Motor Speed Control. Ain Shams Engineering Journal, 5, 803–816.
• ⦁ Sabir, M.M., Ali, T., 2016. Optimal PID Controller Design Through Swarm Intelligence Algorithms for Sun Tracking System. Applied Mathematics and Computation, 274, 690–699.
• ⦁ Rodr´ıguez-Molina, A., Villarreal-Cervantes, M.G., Aldape-P´erez, M., 2017. An Adaptive Control Study for a DC Motor Using Meta- Heuristic Algorithms. IFAC Papers On Line, 50-1, 13114–13120.
• ⦁ El-samahy, A.A., Shamseldin, M.A., 2018. Brushless DC Motor Tracking Control Using Self-Tuning Fuzzy PID Control and Model Reference Adaptive Control. Ain Shams Engineering Journal, 9, 341–352.
• ⦁ Gamazo-Real, J.C., Martínez-Martínez, V., Gomez-Gil, J., 2022. ANN-Based Position and Speed Sensorless Estimation for BLDC Motors. Measurement, 188, 110602.
• ⦁ Şen, Z., 2004. Yapay Sinir Ağları İlkeleri. Su Vakfı Yayınları, İstanbul, 183.
• ⦁ Öztemel, E., 2012. Yapay Sinir Ağları, Third Edition. Papatya Yayıncılık, İstanbul, 232.
• ⦁ Sağıroğlu, Ş., Beşdok, E., Erler, M., 2003. Mühendislikte Yapay Zeka Uygulamaları I, Yapay Sinir Ağları. UFUK Yayıncılık, Kayseri, 426.
• ⦁ Sahroni, A., 2013. Design of Intelligent Control System Based on General Regression Neural Network Algorithm. GSTF Journal on Computing (JoC), 2(4), 103–110.
• ⦁ Pololu Motor Controller Card, https://www.pololu.com/product/1383, Access date: 03.05.2022.
• ⦁ Humusoft Data Acquisition Card, https://www.humusoft.cz/datacq/mf634/, Access date: 03.05.2022.
• ⦁ Karunasingha, D.S.K., 2022. Root Mean Square Error or Mean Absolute Error? Use Their Ratio as Well, Information Sciences, 585, 609–629.
• ⦁ Chicco, D., Warrens, M.J., Jurman, G., 2021. The Coefficient of Determination R-Squared is More Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation. PeerJ Computer Science, 7:e623, https://doi.org/10.7717/peerj-cs.623.
• ⦁ Schubert, A.L., Hagemann, D., Voss, A., Bergmann, K., 2017. Evaluating the Model Fit of Diffusion Models with the Root Mean Square Error of Approximation. Journal of Mathematical Psychology, 77, 29–45.
• ⦁ Myttenaere, M., Golden, B., Grand, B.L., Rossi, F., 2016. Mean Absolute Percentage Error for Regression Models. Neurocomputing, 192, 38–48.
• ⦁ Yavuz, H., Beller, S., 2021. An Intelligent Serial Connected Hybrid Control Method for Gantry Cranes, Mech. Syst. Sig. Process., 146, 107011.