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DC Motorun Hız Kontrolü İçin Meta-Sezgisel Algoritma Tabanlı PID Denetleyici Tasarımı

Year 2021, , 533 - 549, 07.06.2021
https://doi.org/10.17798/bitlisfen.861591

Abstract

Bu makalede DC motorun hız kontrolü için balina optimizasyon (WOA) ve güve-alevi optimizasyon (MFO) algoritmaları kullanılarak en uygun kp, ki, kd PID denetleyici parametreleri belirlenmiştir. PID parametre değerleri belirlenirken her iki algoritma için de hata tabanlı bir amaç fonksiyonu olan hatanın mutlak değerinin zaman ağırlıklı integrali (ITAE) seçilmiştir. İki farklı yaklaşımla tasarlanan PID denetleyicilerin geçici yanıt analizi, frekans analizi, performans indislerinin analizi ve yük bozucu cevabı analizi Matlab programı aracılığıyla gerçekleştirilmiş ve performans karşılaştırılmaları yapılmıştır. Bulunan sonuçlara göre WOA-PID tabanlı denetleyicinin MFO-PID tabanlı denetleyiciye göre daha iyi sonuç verdiği gözlemlenmiştir. Ayrıca WOA-PID denetleyicisinin atom arama (ASO), gri kurt (GWO), yabani ot (IWO), stokastik fraktal arama (SFS) algoritmaları kullanılarak ayarlanmış PID denetleyicilerine göre geçici yanıt karakteristiğini iyileştirdiği, dayanıklılığını arttırdığı ve sistemde yük torkundan kaynaklanan bir değişiklik meydana geldiğinde bu bozulmayı daha kısa sürede toparlamayı başardığı görülmüştür.

References

  • Khanam I., Parmar G. 2017. Application of SFS algorithm in control of DC motor and comparative analysis. In 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), October, 256-261.
  • Khalilpour R., Razmjooy A., Hosseini H., Moallem P. 2011. Optimal control of DC motor using invasive weed optimization (IWO) algorithm. In Majlesi Conference on Electrical Engineering, Majlesi New Town, Isfahan, August, 1-6.
  • Hekimoğlu B. 2019. Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm. IEEE Access, 7: 38100-38114.
  • Agarwal J., Parmar G., Gupta R., Sikander A. 2018. Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor. Microsystem Technologies, 24 (12): 4997-5006.
  • Jain R.V., Aware M.V., Junghare A.S. 2016. Tuning of fractional order PID controller using particle swarm optimization technique for DC motor speed control. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), July, 1-4.
  • Saini R., Parmar G., Gupta R. 2020. SFS based Fractional Order PID Controller (FOPID) for Speed Control of DC Motor. International Journal, 9 (4).
  • Roy A., Srivastava S. 2016. Design of optimal PIλDδ controller for speed control of DC motor using constrained particle swarm optimization. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), March, 1-6.
  • Munagala V.K., Jatoth R.K. 2021. Design of Fractional-Order PID/PID Controller for Speed Control of DC Motor Using Harris Hawks Optimization. In Intelligent Algorithms for Analysis and Control of Dynamical Systems, 103-113.
  • Azman M.A.H., Aris J.M., Hussain Z., Samat A.A.A., Nazelan A.M. 2017. A comparative study of fuzzy logic controller and artificial neural network in speed control of separately excited DC motor. In 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), November, 336-341.
  • Varshney A., Gupta D., Dwivedi B. 2017. Speed response of brushless DC motor using fuzzy PID controller under varying load condition. Journal of Electrical Systems and Information Technology, 4 (2): 310-321.
  • Wang M.S., Chen S.C., Shih C.H. 2018. Speed control of brushless DC motor by adaptive network-based fuzzy inference. Microsystem Technologies, 24 (1): 33-39.
  • Dursun E.H., Durdu A. 2016. Speed control of a DC motor with variable load using sliding mode control. International Journal of Computer and Electrical Engineering, 8 (3): 219-226.
  • Bingul Z., Karahan O. 2018. A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. Journal of the Franklin Institute, 355 (13): 5534-5559.
  • Ekinci S., Hekimoğlu B. 2019. Improved kidney-inspired algorithm approach for tuning of PID controller in AVR system. IEEE Access, 7: 39935-39947.
  • Hekimoğlu B. 2019. Böbrek-ilhamlı Algoritma ile Ayarlanan PID Kontrolör Kullanarak DC Motor Hız Kontrolü. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 8 (2): 652-663.
  • Syafaah L., Pakaya I., Suhardi D., Irfan M. 2017. PID designs using DE and PSO algorithms for damping oscillations in a DC motor speed. In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), September, 1-5.
  • El-Deen A.T., Mahmoud A.H., El-Sawi A.R. 2015. Optimal PID tuning for DC motor speed controller based on genetic algorithm. Int. Rev. Autom. Control, 8 (1): 80-85.
  • Agarwal J., Parmar G., Gupta R. 2017. Application of sine cosine algorithm in optimal control of DC motor and robustness analysis. Wulfenia Journal, 24 (11): 77-95.
  • Ekinci S., Hekimoğlu B., Demirören A., Eker E. 2019. Speed Control of DC Motor Using Improved Sine Cosine Algorithm Based PID Controller. In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), October, 1-7.
  • Das K.R., Das D., Das J. 2015. Optimal tuning of PID controller using GWO algorithm for speed control in DC motor. In 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), October, 108-112.
  • Ekinci S., Izci D., Hekimoğlu B. 2020. PID Speed Control of DC Motor Using Harris Hawks Optimization Algorithm. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), June, 1-6.
  • Ekinci S., Hekimoğlu B., Izci D. 2020. Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor. Engineering Science and Technology, an International Journal. 1-12.
  • Achanta R.K., Pamula V.K. 2017. DC motor speed control using PID controller tuned by jaya optimization algorithm. In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), September, 983-987.
  • Hekimoğlu B., Ekinci S., Demiray V., Doguruci R., Yıldırım A. 2018. Speed control of DC motor using PID controller tuned by salp swarm algorithm. In Proc. IENSC, 1878-1889.
  • Bhatt R., Parmar G., Gupta R., Sikander A. 2019. Application of stochastic fractal search in approximation and control of LTI systems. Microsystem Technologies, 25 (1): 105-114.
  • Mishra A.K., Tiwari V.K., Kumar R., Verma T. 2013. Speed control of DC motor using artificial bee colony optimization technique. In 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE), December, 1-6.
  • Duman S., Maden D., Güvenç U. 2011. Determination of the PID controller parameters for speed and position control of DC motor using gravitational search algorithm. In 2011 7th International Conference on Electrical and Electronics Engineering (ELECO), December, 225-229.
  • Ogata K. 2004. System Dynamics. 4th Ed., Pearson Prentice Hall, Upper Saddle River, NJ, 1-759.
  • Mirjalili S., Lewis A. 2016. The whale optimization algorithm. Advances in engineering software, 95: 51-67.
  • Mirjalili S. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89: 228-249.
Year 2021, , 533 - 549, 07.06.2021
https://doi.org/10.17798/bitlisfen.861591

Abstract

References

  • Khanam I., Parmar G. 2017. Application of SFS algorithm in control of DC motor and comparative analysis. In 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON), October, 256-261.
  • Khalilpour R., Razmjooy A., Hosseini H., Moallem P. 2011. Optimal control of DC motor using invasive weed optimization (IWO) algorithm. In Majlesi Conference on Electrical Engineering, Majlesi New Town, Isfahan, August, 1-6.
  • Hekimoğlu B. 2019. Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm. IEEE Access, 7: 38100-38114.
  • Agarwal J., Parmar G., Gupta R., Sikander A. 2018. Analysis of grey wolf optimizer based fractional order PID controller in speed control of DC motor. Microsystem Technologies, 24 (12): 4997-5006.
  • Jain R.V., Aware M.V., Junghare A.S. 2016. Tuning of fractional order PID controller using particle swarm optimization technique for DC motor speed control. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), July, 1-4.
  • Saini R., Parmar G., Gupta R. 2020. SFS based Fractional Order PID Controller (FOPID) for Speed Control of DC Motor. International Journal, 9 (4).
  • Roy A., Srivastava S. 2016. Design of optimal PIλDδ controller for speed control of DC motor using constrained particle swarm optimization. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), March, 1-6.
  • Munagala V.K., Jatoth R.K. 2021. Design of Fractional-Order PID/PID Controller for Speed Control of DC Motor Using Harris Hawks Optimization. In Intelligent Algorithms for Analysis and Control of Dynamical Systems, 103-113.
  • Azman M.A.H., Aris J.M., Hussain Z., Samat A.A.A., Nazelan A.M. 2017. A comparative study of fuzzy logic controller and artificial neural network in speed control of separately excited DC motor. In 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), November, 336-341.
  • Varshney A., Gupta D., Dwivedi B. 2017. Speed response of brushless DC motor using fuzzy PID controller under varying load condition. Journal of Electrical Systems and Information Technology, 4 (2): 310-321.
  • Wang M.S., Chen S.C., Shih C.H. 2018. Speed control of brushless DC motor by adaptive network-based fuzzy inference. Microsystem Technologies, 24 (1): 33-39.
  • Dursun E.H., Durdu A. 2016. Speed control of a DC motor with variable load using sliding mode control. International Journal of Computer and Electrical Engineering, 8 (3): 219-226.
  • Bingul Z., Karahan O. 2018. A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. Journal of the Franklin Institute, 355 (13): 5534-5559.
  • Ekinci S., Hekimoğlu B. 2019. Improved kidney-inspired algorithm approach for tuning of PID controller in AVR system. IEEE Access, 7: 39935-39947.
  • Hekimoğlu B. 2019. Böbrek-ilhamlı Algoritma ile Ayarlanan PID Kontrolör Kullanarak DC Motor Hız Kontrolü. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 8 (2): 652-663.
  • Syafaah L., Pakaya I., Suhardi D., Irfan M. 2017. PID designs using DE and PSO algorithms for damping oscillations in a DC motor speed. In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), September, 1-5.
  • El-Deen A.T., Mahmoud A.H., El-Sawi A.R. 2015. Optimal PID tuning for DC motor speed controller based on genetic algorithm. Int. Rev. Autom. Control, 8 (1): 80-85.
  • Agarwal J., Parmar G., Gupta R. 2017. Application of sine cosine algorithm in optimal control of DC motor and robustness analysis. Wulfenia Journal, 24 (11): 77-95.
  • Ekinci S., Hekimoğlu B., Demirören A., Eker E. 2019. Speed Control of DC Motor Using Improved Sine Cosine Algorithm Based PID Controller. In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), October, 1-7.
  • Das K.R., Das D., Das J. 2015. Optimal tuning of PID controller using GWO algorithm for speed control in DC motor. In 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), October, 108-112.
  • Ekinci S., Izci D., Hekimoğlu B. 2020. PID Speed Control of DC Motor Using Harris Hawks Optimization Algorithm. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), June, 1-6.
  • Ekinci S., Hekimoğlu B., Izci D. 2020. Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor. Engineering Science and Technology, an International Journal. 1-12.
  • Achanta R.K., Pamula V.K. 2017. DC motor speed control using PID controller tuned by jaya optimization algorithm. In 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), September, 983-987.
  • Hekimoğlu B., Ekinci S., Demiray V., Doguruci R., Yıldırım A. 2018. Speed control of DC motor using PID controller tuned by salp swarm algorithm. In Proc. IENSC, 1878-1889.
  • Bhatt R., Parmar G., Gupta R., Sikander A. 2019. Application of stochastic fractal search in approximation and control of LTI systems. Microsystem Technologies, 25 (1): 105-114.
  • Mishra A.K., Tiwari V.K., Kumar R., Verma T. 2013. Speed control of DC motor using artificial bee colony optimization technique. In 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE), December, 1-6.
  • Duman S., Maden D., Güvenç U. 2011. Determination of the PID controller parameters for speed and position control of DC motor using gravitational search algorithm. In 2011 7th International Conference on Electrical and Electronics Engineering (ELECO), December, 225-229.
  • Ogata K. 2004. System Dynamics. 4th Ed., Pearson Prentice Hall, Upper Saddle River, NJ, 1-759.
  • Mirjalili S., Lewis A. 2016. The whale optimization algorithm. Advances in engineering software, 95: 51-67.
  • Mirjalili S. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-based systems, 89: 228-249.
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Ali Kıvanç Şahin 0000-0003-4539-6419

Ömür Akyazı 0000-0001-6266-2323

Erdinc Sahın 0000-0002-9740-599X

Oğuzhan Çakır 0000-0002-6576-8710

Publication Date June 7, 2021
Submission Date January 15, 2021
Acceptance Date March 6, 2021
Published in Issue Year 2021

Cite

IEEE A. K. Şahin, Ö. Akyazı, E. Sahın, and O. Çakır, “DC Motorun Hız Kontrolü İçin Meta-Sezgisel Algoritma Tabanlı PID Denetleyici Tasarımı”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 10, no. 2, pp. 533–549, 2021, doi: 10.17798/bitlisfen.861591.



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