Analysis of the Sudden Load Change Responses of the Data-Driven Control and Model-Based Control Methods for DC Motor Control

Year 2024, EARLY VIEW, 1 - 1

Güray Sonugür

https://doi.org/10.2339/politeknik.1326256

Abstract

In Direct Current (DC) motor speed controllers, resisting disturbances and tracking the set point with minimum error against any external factors is critical. The most common type of DC motor disturbance is sudden load changes. As a result, controllers must build a quick and effective response to sudden load changes while deviating as little as possible from the reference values. The responses of model-based and data-driven control approaches to sudden load changes in DC motors are studied in this paper. Data-driven control (DDC) is a learning-based control system that designs and optimizes the controller based on collected input-output data. A mathematical model of the system is calculated in model-based control (MBC). Within the scope of the study, the Proportional-Integral-Derivative (PID) method is analyzed both model-based and data-driven. In addition, artificial neural networks (ANN) and nonlinear autoregressive with exogenous input (NARX) controllers are also investigated as data-driven methods. Thus, the performances of three different approaches for DC motor speed control: model-based, data-driven, and data-driven + time series were investigated. In the experimental studies, real motors were used, not simulations, and the experiments were carried out in real-time using permanent magnet DC motors with 100 rpm (DAM1) and 300 rpm (DAM2) speeds. The results were presented using total normalized error, rise time, and maximum percentage overshoot metrics, and the methods' performance was discussed.

Keywords

DC motor control, Data Driven Control, Model-based Control, NARX, ANN

DA Motor Kontrolünde Veri Güdümlü ve Model Tabanlı Yöntemlerin Ani Yük Değişimlerine Karşı Tepkilerinin Analizi

Abstract

Doğru Akım (DA) motor hız denetleyicilerinde bozucu etkilere karşı direnç gösterme ve her türlü dış etki karşısında referans noktasını en az hata ile takip etmek kritik öneme sahiptir. DA motorlarda en sık karşılaşılan bozucu etki ani yük değişimleridir. Bu nedenle denetleyicilerin ani yük değişimlerine karşı hızlı ve etkili bir yanıt oluşturulması ve referans değerden en az sapmayı gerçekleştirmesi gerekir. Bu çalışmada DA motorlarda meydana gelebilecek ani yük değişimlerine karşı model tabanlı ve veri güdümlü yöntemlerin yanıtları analiz edilmiştir. Veri güdümlü kontrol (VGK), denetleyiciyi tasarlamak ve optimize etmek için toplanan giriş-çıkış verilerini kullanan öğrenme tabanlı bir kontrol yöntemidir. Model tabanlı kontrol (MTK) yönteminde ise, kontrol edilecek sistemin matematiksel modeli hesaplanır. Çalışma kapsamında model tabanlı yöntem olarak Oransal-İntegral-Türev (PID), veri güdümlü yöntemler olarak yapay sinir ağları (YSA) ve kontrol süreçlerinde zaman serilerini de dikkate alan dışsal girdili otoregresif sinir ağları (NARX) denetleyiciler incelenmiştir. Böylece DA motor hız kontrolünde model tabanlı, veri güdümlü ve veri güdümlü + zaman serili olmak üzere üç farklı yaklaşımın performansları incelenmiştir. Deneysel çalışmalarda simülasyon değil gerçek motorlar kullanılmış ve deneyler 100 rpm (DAM1) ve 300 rpm (DAM2) hızına sahip kalıcı mıknatıslı DA motorlar kullanılarak gerçek zamanlı olarak gerçekleştirilmiştir. Elde edilen sonuçlar, toplam normalize hata, yükselme zamanı ve maksimum yüzde aşma performans ölçütleri kullanılarak sunulmuş ve yöntemlerin başarılı ve başarısız yönleri tartışılmıştır.

Keywords

DA motor kontrolü, Veri güdümlü kontrol, Model tabanlı kontrol, NARX, YSA

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