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

Year 2024, Volume: 27 Issue: 5, 1721 - 1732, 02.10.2024

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

References

• [1] Carlet, P.G., Favato, A., Bolognani, S., Dorfler, F., “Data-driven predictive current control for synchronous motor drives,” ECCE 2020 - IEEE Energy Conversion Congress and Exposition, 5148–5154, Institute of Electrical and Electronics Engineers Inc., (2020).
• [2] Özlük, F., Sayan, H., Üniversitesi, G., et al., “Matlab GUI ile DA Motor için PID Denetleyicili Arayüz Tasarımı,” Journal of Advanced Technology Sciences, 3(3): 10–18, (2013).
• [3] Manjunatha, H.K.R., Immanuel, J., Parvathi, C.S., Bhaskar, P., Sudheer, L.S., “Implementation of PID controller in MATLAB for real time DC motor speed control system”, Sensors and Transducers 126(3): 110–118, (2011).
• [4] Ekinci, S., Hekimoglu, B., Demiroren, A., Eker, E., “Speed Control of DC Motor Using Improved Sine Cosine Algorithm Based PID Controller,” 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 1-7, (2019).
• [5] Gökçe, B., Koca, Y.B., Aslan, Y., “Doğru Akım Motorunun PID ile Hız Kontrolü ve Zorlamalı Yükler Altında Performans Analizi,” European Journal of Science and Technology, 21: 549–554, (2021).
• [6] Zhang, S., Gu, W., Hu, Y., Du, J., Chen, H., “Angular speed control of brushed DC motor using nonlinear method: Design and experiment,” Chinese Control Conference - CCC, 1045–1050, (2016).
• [7] Çavdar, B., Sahın, E., Nuroglu, F., "Doğru Akım Motoru Hız Kontrolü için SAA Tabanlı Kesir Dereceli PI-PD Eklemeli Denetleyici Tasarımı", Politeknik Dergisi, 1-1, (2023).
• [8] Maarif, A., Setiawan, N.R., “Control of DC Motor Using Integral State Feedback and Comparison with PID: Simulation and Arduino Implementation,” Journal of Robotics and Control (JRC) 2(5): 456–461, (2021).
• [9] Kumar Bansal, U., Narvey, R., “Speed Control of DC Motor Using Fuzzy PID Controller”, Advance in Electronic and Electric Engineering, 1209–1220 (2013).
• [10] Thomas, N., Poongodi, P., “Position Control of DC Motor Using Genetic Algorithm Based PID Controller”, Proceedings of the World Congress on Engineering 2009 Vol II, WCE, London, 1–5, (2009).
• [11] Yüksek, G., Naci METE, A., Alkaya, A., “PID parametrelerinin LQR ve GA tabanlı optimizasyonu: sıvı seviye kontrol uygulaması,” Politeknik Dergisi, 23(4): 1111–1119, (2020).
• [12] Rahayu, E.S., Ma’arif, A., Cakan, A., “Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor”, International Journal of Robotics and Control Systems, 2(2): 435–447, (2022).
• [13] Weerasooriya, S., El-Sharkawi, M.A., “Identification and Control of a DC Motor Using Backpropagation Neural Networks”, IEEE Transactions on Energy Conversion, 6(4): 663–669, (1991).
• [14] Bulut, M., "Bulanık Ters Model Kullanılarak Doğru Akım Motor Sürücüsü için Referans Model Temelli Uyarlanabilir Bulanık Denetleyici", Politeknik Dergisi, 26(2): 593-602, (2023).
• [15] Chaudhary, H., Khatoon, S., Singh, R., “ANFIS based speed control of DC motor”, 2nd IEEE International Conference on Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity, CIPECH, Ghaziabad, India, 63–67, (2017).
• [16] Alkurawy, L.E.J., Khamas, N., “Model predictive control for DC motors”, 1st International Scientific Conference of Engineering Sciences - 3rd Scientific Conference of Engineering Science, ISCES, Diyala, Iraq, 56–61, (2018).
• [17] Emi̇roğlu, A., Yaren, T., Ki̇zi̇r, S., "Kendinden Ayarlamalı Denetleyici ile DA Motor Hız Kontrolü", Politeknik Dergisi, 25(2): 757-765, (2022).
• [18] Naung, Y., Schagin, A., Oo, H.L., Ye, K.Z., Khaing, Z.M., “Implementation of data driven control system of DC motor by using system identification process”, Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus, St. Petersburg, Russia, 1801–1804, (2018).
• [19] Supeni, E., Yassin, I.M., Ahmad, A., Abdul Rahman, F.Y., “NARMAX identification of DC motor model using repulsive particle swarm optimization”, Proceedings of 2009 5th International Colloquium on Signal Processing and Its Applications- CSPA, Kuala Lumpur, Malaysia, 1–7, (2009).
• [20] Moradi, M., Abhari, S., Dehghan, F., “DC motor control with comparative method for controller validation”, 3rd International Conference on Advanced Computer Control, ICACC, 465–469 (2011).
• [21] Chandramouleeswaran, G., Prabhu, M., Rajalakshmi, M., et al., “ANN based PID controlled brushless DC drive system”, Int. J. on Electrical and Power Engineering, 3(1): 45–49, (2012).
• [22] Hamoodi, S.A., Sheet, I.I., Mohammed, R.A., “A Comparison between PID controller and ANN controller for speed control of DC Motor”, 2nd International Conference on Electrical, Communication, Computer, Power and Control Engineering- ICECCPCE, Mosul, Iraq, 221–224, (2019).
• [23] Doğruer, T., “DC Motorun Hız Kontrolü İçin Kesir Dereceli Pıd Kontrolör Tasarımı Ve Dayanıklılık Analizi”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 19: 15–28, (2023).
• [24] Al Nisa, S., Mathew, L., Chatterji, S., “Comparative Analysis of Speed Control of DC Motor Using AI Technique,” International Journal of Engineering Research and Applications (IJERA), 3(3): 1137–1146, (2013).
• [25] Sonugür, G., “A Review of quadrotor UAV: Control and SLAM methodologies ranging from conventional to innovative approaches”, Robotics and Autonomous Systems, 161, 104342, (2023).
• [26] Harshitha, S., Shamanth, S., Chari, A.K., “A Review of Various Controller Techniques Designed for the Operational Control of DC and Servo Motors”, Journal of Physics: Conference Series, 2273(1): 012001, (2022).
• [27] Junaid, A. Bin, Konoiko, A., Zweiri, Y., Sahinkaya, M.N., Seneviratne, L., “Autonomous wireless self-charging for multi-rotor unmanned aerial vehicles”, Energies, 10(6): 1–14, (2017).
• [28] Castillo-Zamora, J.J., Camarillo-Gomez, K.A., Perez-Soto, G.I., Rodriguez-Resendiz, J., “Comparison of PD, PID and sliding-mode position controllers for v-tail quadcopter stability”, IEEE Access, 6: 38086–38096, (2018).
• [29] Vural, A.M., Bayindir, K.C., “Optimization of parameter set for STATCOM control system”, 2010 IEEE PES Transmission and Distribution Conference and Exposition: Smart Solutions for a Changing World, New Orleans, LA, USA, 1-6, (2010).
• [30] J Ziegler, J.G., Nichols, N.B., “Optimum Settings for Automatic Controllers”, Journal of Fluids Engineering, 64(8): 759–765, (1942).
• [31] Hou, Z.S., Wang, Z., “From model-based control to data-driven control: Survey, classification and perspective”, Information Sciences, 235: 3–35, (2013).
• [32] Cohen, C., Coon, C.G., “Theoretical considerations of optimal control”, Journal of the Franklin Institute, 255(4): 261–297, (1953).
• [33] Mohamed, T.L.T., Mohamed, R.H.A., Mohamed, Z., “Development of auto tuning PID controller using Graphical User Interface (GUI)” 2010 2nd International Conference on Computer Engineering and Applications-ICCEA, 1: 491–495, (2010).
• [34] El-Khouly, F.M., Sharaf, A.M., Abdel-Ghaffar, A.S., Mohammed, A.A., “Adaptive neural network speed controller for permanent magnet DC motor drives”, Proceedings of the Annual Southeastern Symposium on System Theory, 416–420, (1994).
• [35] Ismeal, G.A., Kyslan, K., Fedák, V., “DC motor identification based on Recurrent Neural Networks”, Proceedings of the 16th International Conference on Mechatronics, Mechatronika, Brno, Czech Republic, 701–705, (2014).
• [36] Munagala, V.K., Jatoth, R.K., “A novel approach for controlling DC motor speed using NARXnet based FOPID controller”, Evolving Systems, (2022).
• [37] Chertovskikh, P.A., Seredkin, A. V., Gobyzov, O.A., Styuf, A.S., Pashkevich, M.G., Tokarev, M.P., “An adaptive PID controller with an online auto-tuning by a pretrained neural network”, Journal of Physics: Conference Series, 1359, 012090, (2019).
• [38] Arı, A., Aktaş, M., Yönetken, A., Doğan, R., “Güneş Işınım Tahmininde NARX Modelinin Uygulanması”, International Journal of Engineering Technology and Applied Science, 4(1): 1–6, (2021).
• [39] Tatli, A., Kahvecioğlu, S., “NARX Neural Networks Based Time Series Prediction for Amount of Airworthiness Time”, National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), Bursa, Turkey, 8–12, (2016).