Sezgisel Optimizasyon Kullanarak DC Motor Hız Kontrol Performansının Artırılması ve Kontrol Yöntemlerinin Karşılaştırmalı Analizi

Year 2024, , 2220 - 2244, 23.10.2024

Beytullah Bozali , Nasser Bandar Nasser Al Saremi , Ali Öztürk

https://doi.org/10.29130/dubited.1505316

Abstract

Doğru Akım (DA) motorları, endüstriyel uygulamalardan ev aletlerine kadar geniş bir yelpazede kullanılan, elektrik enerjisini mekanik enerjiye dönüştüren önemli bir bileşendir. DA motor hız kontrolü, endüstriyel süreçlerde verimliliği artırmak, hassas hareketleri gerçekleştirmek ve enerji tüketimini optimize etmek için önemli bir role sahiptir. Bu çalışmada, yaygın kullanım alanlarına sahip DA motorlarının hız kontrolü için çeşitli kontrol yöntemleri ve parametre optimizasyon teknikleri sistematik bir şekilde analiz edilmiştir. Çalışmanın amacı DA motorların farklı hızlarda gerçek zamanlı olarak izleyerek belirlenen hedef hıza ulaşmasını sağlamak ve değişken yükler veya dış etkenlerden kaynaklanan dalgalanmaları minimize etmek için etkili bir kontrol stratejisi geliştirmektir. Çalışmamızda Oransal-İntegral-Türev (PID), Oransal-İntegral (PI), ve Oransal- Türev (PD) kontrol yöntemleri kullanılmıştır. Bu kontrolörlerin parametreleri, Matlab Tuned, yeni nesil sezgisel optimizasyon yöntemi olan Çita Optimizasyon (CO) Algoritması ve geniş kabul görmüş optimizasyon yöntemi olan Parçacık Sürü Optimizasyonu (PSO) kullanılarak ayarlanmıştır. Kontrolörlerin performanslarını, Hatanın Mutlak Değerinin İntegrali (IAE), Hata Karenin İntegrali (ISE) ve Zaman Mutlak Hatanın İntegrali (ITAE) gibi kriterler kullanılarak belirlenmiştir. Elde edilen sonuçlar göre, CO Algoritması kullanılarak belirlenen PID, PI ve PD kontrol parametrelerinin, Matlab Tuned ve PSO yöntemleri kullanılarak oluşturulan kontrolörlerden daha iyi performans gösterdiği bulunmuştur. CO Algoritması gibi yeni optimizasyon yöntemlerinin, kontrol sistemlerinin performansını artırmak için önemli bir potansiyel taşıdığını bulunmuştur. Bu çalışma sayesinde, endüstriyel süreçlerde DA motor hız kontrolünün optimize edilmesi için pratik bir yaklaşım sunmaktadır. Sonuç olarak, CO Algoritmasıyla belirlenen kontrol parametrelerinin, diğer optimizasyon yöntemlerine göre DA motor hız kontrolünde ve kontrol sistemlerinin performansını iyileştirmede önemli potansiyele sahip olduğu bulunmuştur.

Keywords

CO Algoritması, PSO, PID, PI, PD, DA Motor Kontrol

Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods

Abstract

Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. DC motor speed control has an important role in industrial processes to increase efficiency, realize precise movements and optimize energy consumption. In this study, various control methods and parameter optimization techniques for speed control of DC motors, which have a wide range of applications, have been systematically analyzed. The aim of the study is to develop an effective control strategy to ensure that DC motors reach the determined target speed by monitoring them in real time at different speeds and to minimize fluctuations caused by variable loads or external factors. In our study, Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Proportional-Derivative (PD) control methods were used. The parameters of these controllers were tuned using Matlab Tuned, The Cheetah Optimizer (CO) Algorithm, a new generation heuristic optimization method, and Particle Swarm Optimization (PSO), a widely accepted optimization method. The performances of the controllers were determined using criteria such as Integral of Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE). According to the results obtained, it was found that the PID, PI and PD control parameters determined using the CO Algorithm performed better than the controllers created using Matlab Tuned and PSO methods. New optimization methods, such as the CO Algorithm, have been found to have significant potential to improve the performance of control systems. Thanks to this study, it offers a practical approach for optimizing DC motor speed control in industrial processes. As a result, it has been found that the control parameters determined by the CO Algorithm have significant potential in improving the performance of DC motor speed control and control systems compared to other optimization methods.

Keywords

CO Algorithm, PSO, PID, PI, PD, DC Motor Control

References

• [1] N. Baćac, V. Slukić, M. Puskaric, B. Štih, E. Kamenar, and S. Zelenika, “Comparison of different DC motor positioning control algorithms,” 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 2014.
• [2] S. Tufenkci, B. Baykant Alagoz, G. Kavuran, C. Yeroglu, N. Herencsar, and S. Mahata, “A theoretical demonstration for reinforcement learning of PI control dynamics for optimal speed control of DC motors by using Twin Delay Deep Deterministic Policy Gradient Algorithm,” Expert Syst. Appl., vol. 213, pp. 1–16, 2023.
• [3] H. X. Wu, S. K. Cheng, and S. M. Cui, “A controller of brushless DC Motor for electric vehicle,” In 2004 12th Symposium on Electromagnetic Launch Technology, Snowbird, UT, USA. 2004.
• [4] N. B. Berahim, “Development of PID Voltage Control for DC Motor Using Arduino,” M.S. thesis, Department of Electrical and Electronic Engineering, Tun Hussein Onn University, Malaysia, 2014.
• [5] W. Cui, Y. Gong, and M. H. Xu, “A permanent magnet brushless DC motor with bifilar winding for automotive engine cooling application,” IEEE Trans. Magn., vol. 48, no. 11, pp. 3348–3351, 2012.
• [6] K. Vanchinathan and N. Selvaganesan, “Adaptive fractional order PID controller tuning for brushless DC motor using Artificial Bee Colony algorithm,” Results in Control and Optimization., vol. 4, no. 100032, pp. 1–18, 2021.
• [7] A. Rajasekhar, R. Kumar Jatoth, and A. Abraham, “Design of intelligent PID/PIλDμ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm,” Eng. Appl. Artif. Intell., vol. 29, pp. 13–32, 2014.
• [8] S. Khubalkar, A. Junghare, M. Aware, and S. Das, “Modeling and control of a permanent-magnet brushless DC motor drive using a fractional order proportional-integral-derivative controller,” Turkish J. Electr. Eng. Comput. Sci., vol. 25, no. 5, pp. 4223–4241, 2017.
• [9] A. Bisoi, A. K. Samantaray, and R. Bhattacharyya, “Control strategies for DC motors driving rotor dynamic systems through resonance,” Journal of Sound and Vibration, vol. 411, pp. 304–327, 2017.
• [10] H. Ben Abdeljawed and L. El Amraoui, “Simulation and rapid control prototyping of DC powered universal motors speed control: Towards an efficient operation in future DC homes,” Engineering Science and Technology, an International Journal, vol. 34, pp. 1–7, 2022.
• [11] Q. Zhang, B. Wen, and Y. He, “Rotational speed monitoring of brushed DC motor via current signal,” Measurement, vol. 184, no.109890, pp. 1–11, 2021.
• [12] K. Premkumar, and B. V. Manikandan, “Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor,” Neurocomputing, vol. 157, pp. 76–90, 2015.
• [13] R. Arivalahan, S. Venkatesh, and T. Vinoth, “An effective speed regulation of brushless DC motor using hybrid approach,” Advances in Engineering Software., vol. 174, no. 103321, pp. 1–15, 2022.
• [14] E. A. Ramadan, M. El-Bardini, and M. A. Fkirin, “Design and FPGA-implementation of an improved adaptive fuzzy logic controller for DC motor speed control,” Ain Shams Engineering Journal, vol. 5, no. 3, pp. 803–816, 2014.
• [15] E. Batan, “Matlab simulink ortamında kullanılabilen arduino temelli kontrol deney seti tasarımı,” Yüksek lisans tezi, İmalat Mühendisliği Ana Bilim Dalı, Tarsus Üniversitesi, Mersin, Türkiye, 2019.
• [16] K. Premkumar, and B. V. Manikandan, “Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System,” Applied Soft Computing Journal, vol. 32, pp. 403–419, 2015.
• [17] A. Rodríguez-Molina, M. G. Villarreal-Cervantes, J. Álvarez-Gallegos, and M. Aldape-Pérez, “Bio-inspired adaptive control strategy for the highly efficient speed regulation of the DC motor under parametric uncertainty,” Applied Soft Computing Journal, vol. 75, pp. 29–45, 2019.
• [18] A. Bahadir, and Ö. Aydoğdu, “Modeling of a brushless dc motor driven electric vehicle and its pid-fuzzy control with dSPACE,” Sigma Journal of Engineering and Natural Sciences, vol. 41, no. 1, pp. 156–177, 2023.
• [19] B. Suna, “D.C Motorda Kontrol Yöntemlerinin Simülasyonu,” Yüksek lisans tezi, Elektrik Mühendisliği Bölümü, Sakarya Üniversitesi, Sakarya, Türkiye, 2009.
• [20] C. Guo-qiang, and Z. Zhi-rui, “Mechanical analysis of the industrial robot to upgrade to the gaming robot,” 13th Global Congress on Manufacturing and Management, GCMM 2016, 2017, pp. 1077–1083.
• [21] A. Derdiyok, B. Soysal, F. Arslan, Y. Ozoglu, and M. Garip “An adaptive compensator for a vehicle driven by DC motors,” Journal of The Franklin Institute., vol. 342, pp. 273–283, 2005.
• [22] T. Szecsi, “A DC motor based cutting tool condition monitoring system,” Journal of Materials Processing Technology, vol. 93, pp. 350–354, 1999.
• [23] A. Bawdaka, and İ. Kaya, “DC Motor Sürücüsü İçin Model Öngörülü Denetleyici Tasarımı,” DÜMF Mühendislik Dergisi, c. 10, s. 3, ss. 899–910, 2019.
• [24] Pololu. (2024, Feb 02). Metal Gearmotor 25Dx64L mm HP 6V with 48 CPR Encoder [Online]. Available: https://www.pololu.com/product/2273
• [25] D. Tilbury, B. Messner, R. Hill, J. D. Taylor, S. Das, and M. Hagenow. (2024, Feb 02). Time-Response Analysis of a DC Motor [Online]. Available: http://ctms.engin.umich.edu/CTMS/index.php?aux=Activities_DCmotor
• [26] A. Ozturk, B. Bozali, and S. Tosun, “Investigating voltage and frequency stability problems in the electrical power system using Gravitational Search Algorithms,” Journal of Optoelectronics and Advanced Materials, vol. 18, no. 1–2, pp. 153–159, 2016.
• [27] M. S. Tavazoei, “Notes on integral performance indices in fractional-order control systems,” Journal of Process Control vol. 20, no. 3, pp. 285–291, 2010.
• [28] A. A. Kesarkar, and N. Selvaganesan, “Tuning of optimal fractional-order PID controller using an artificial bee colony algorithm,” Systems Science and Control Engineering, vol. 3, no. 1, pp. 99–105, 2015.
• [29] B. Bozali, “Elektrik Güç Sistemlerinde Kararlılık Problemlerinin Yerçekimi Algoritması ile İncelenmesi,” Yüksek lisans tezi, Elektrik Eğitimi Bölümü, Düzce Üniversitesi, Düzce, Türkiye, 2012.
• [30] J. Kennedy, and E. Russell, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, 1995, pp. 1942–1948.
• [31] P. Dutta and S. K. Nayak, “Grey Wolf Optimizer Based PID Controller for Speed Control of BLDC Motor,” Journal of Electrical Engineering and Technology, vol. 16, no. 2, pp. 955–961, 2021.
• [32] H. Yiğit, S. Ürgün, and S. Mirjalili, “Comparison of recent metaheuristic optimization algorithms to solve the SHE optimization problem in MLI,” Neural Comput. Appl., vol. 35, no. 10, pp. 7369–7388, 2023.
• [33] N. Subaş, “Sürekli/İkili Parçacık Sürü Optimizasyonu ve Destek Vektör Makinelerinin Hibrit Kullanımı ile Özellik Seçimi,” Yüksek lisans tezi, İstatistik Programı, Mimar Sinan Güzel Sanatlar Üniversitesi, İstanbul, Türkiye, 2019.
• [34] B. Bozali, “Türkiye 400 kV’luk Güç Sistemi İçin Sezgisel Yöntemler Kullanılarak Optimal Fazör Ölçüm Birimlerinin Yerleşim Noktalarının Belirlenmesi,” Doktora tezi, Elektrik Elektronik Mühendisliği Bölümü, Düzce Üniversitesi, Düzce, Türkiye, 2022.
• [35] N. H. A. Rahman, A. F. Zobaa, and M. Theodoridis, “Improved BPSO for optimal PMU placement,” Proceedings of the Universities Power Engineering Conference, 2015, pp. 1–4.
• [36] L. Abualigah, M. A. Elaziz, A. M. Khasawneh, M. Alshinwan, R. A. Ibrahim, M. A. A. Al-qaness, S. Mirjalili, P. Sumari, and A. H. Gandomi, “Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results,” Neural Computing and Applications, vol. 34, no. 6, pp. 4081–4110, 2022.
• [37] Baeldung. (2024, Feb 21). How does particle swarm optimization work [Online]. Available: https://www.baeldung.com/cs/pso
• [38] H. Jagtap, A. Bewoor, R. Kumar, M. H. Ahmadi, and G. Lorenzini, “Markov-based performance evaluation and availability optimization of the boiler–furnace system in coal-fired thermal power plant using PSO,” Energy Reports, vol. 6, pp. 1124–1134, 2020.
• [39] E. S. Ali, “Optimization of Power System Stabilizers using BAT search algorithm,” International Journal of Electrical Power & Energy Systems, vol. 61, pp. 683–690, 2014.
• [40] D. K. Sambariya, and R. Prasad, “Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat algorithm,” International Journal of Electrical Power & Energy Systems, vol. 61, pp. 229–238, 2014.
• [41] M. A. Akbari, M. Zare, R. Azizipanah-abarghooee, S. Mirjalili, and M. Deriche, “The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems,” Nature Publishing Group, UK, Nov. 12:10953, 2022.
• [42] Z. A. Memon, M. A. Akbari, and M. Zare, “An improved cheetah optimizer for accurate and reliable estimation of unknown parameters in photovoltaic cell and module models,” Applied Sciences, vol. 13, no. 18, 2023.