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Tuning Proportional Integral Derivative Controller Parameters with Modified Radial Movement Optimization

Year 2021, Issue: 23, 9 - 21, 30.04.2021
https://doi.org/10.31590/ejosat.830467

Abstract

In this article, modified radial movement optimization (MRMO) is proposed as a new variant of radial movement optimization (RMO). Also, for the three selected test systems, the gain factors of the PID controller were optimized by the proposed method (MRMO), particle swarm optimization (PSO), radial movement optimization (RMO), differential evolution (DE), and genetic algorithm (GA). With heuristic optimization methods, four different error area-based performance criteria were used in parameter tuning: Integral of square error, integral of absolute value of error, time-weighted integral of square error, and time-weighted integral of absolute value of error. First, the performance of the PID controller tuned by these five heuristic optimization methods (MRMO, PSO, RMO, DE, and GA) was compared according to the selected error area criteria, and then the performances of the optimization methods were compared with each other and the results obtained were analyzed. There are many studies in the literature regarding the tuning of PID controller parameters using PSO, DE, GA, and RMO. However, there is no study on the optimization of the gain constants of the PID controller with the proposed MRMO. This is the most important contribution of this study presented to the current literature. 

References

  • Boudardara, F., & Gorkemli, B. (2018). Application of artificial bee colony programming to two trails of the artificial ant problem. 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, Turkey, 1-6.
  • Çeven, S., & Albayrak, A. (2020). Çift ters sarkaç sisteminin kontrolü için PID ve LQR kontrolcü tasarımlarının modellenmesi. Avrupa Bilim ve Teknoloji Dergisi, (Special Issue), 323-330.
  • Deb, K., (1999). An introduction to genetic algorithms. Sadhana, 24(4), 293-315.
  • Denizci, A., & Ulu, C. (2020). Fuzzy cognitive map based PID controller design. Avrupa Bilim ve Teknoloji Dergisi, (Special Issue), 165-171.
  • Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Japan, 39-43.
  • Eberhart, R. C., Simpson, P. K., & Dobbins, R. W., (1996). Computational intelligence PC tools. Academic Press.
  • Fokas, A., (2002). A new transform method for evolution partial differential equations. IMA Journal of Applied Mathematics, 67(6), 559-590.
  • Gille, J., & Paquet, J., (1962). Subharmonic oscillations in on-off control systems. Transactions of the American Institute of Electrical Engineers Part II: Applications and Industry, 81(4), 210-216.
  • Gür, H., & Furat, M., (2020). Özelleştirilmiş uygunluk fonksiyonu tabanlı su döngüsü algoritması ile PID parametrelerinin optimizasyonu. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 332-341.
  • Hägglund, T., & Åström, K. J., (2004). Revisiting the Ziegler-Nichols step response method for PID control. Journal of Process Control, 14(6), 635–650.
  • Heppner, H., & Grenander, U., (1990). A stochastic non-linear model for coordinated bird flocks. AAAS.
  • Jing, H., Liu, Z., & Chen, H., (2011). A switched control strategy for antilock braking system with on/off valves. IEEE Transactions on Vehicular Technology, 60(4), 1470-1484.
  • Jin, L., & Feng, Q., (2018). Improved radial movement optimization to determine the critical failure surface for slope stability analysis. Environmental Earth Sciences, 77(16). 564-576.
  • Jin, L., Zhang, H., & Feng, Q. (2019). Application of improved radial movement optimization for calculating the upper bound of ultimate bearing capacity of shallow foundation on unsaturated soil. Computers And Geotechnics, 109, 82-88.
  • Kaya, R., & Furat, M., (2020). Three-channel cost function based artificial bee colony algorithm for PID tuning. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 382-392.
  • Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In Proceedings of the Conference on Systems Man and Cybernetics, USA, 4104–4109.
  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization, IV IEEE International Conference on Neural Networks, USA, 1942–1948.
  • Koçer, B., (2017). İstatistiksel olarak yönlendirilen yapay arı kolonisi algoritması. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 5(2), 153-169.
  • Koswara, A., & Nagy, Z., (2017). ON–OFF feedback control of plug-flow crystallization: A Case of Quality-by-Control in Continuous Manufacturing. IEEE Life Sciences Letters, 3(1), 1-4.
  • Köse, E. & Coşkun, S., (2020). Time-delay AVR system analysis using PSO-based PID controller. European Journal of Science and Technology, 18, 981-991.
  • Köse, O. & Oktay, T. (2020). Investigation of the effect of differential morphing on lateral flight by using PID algorithm in quadrotors. Avrupa Bilim ve Teknoloji Dergisi, 18, 636-644.
  • Lee, T., (2008). Optimal wind–battery coordination in a power system using evolutionary iteration particle swarm optimisation. IET Generation Transmission & Distribution, 2(2), 291-300.
  • Mills, K., Filliben, J., & Haines, A., (2015). Determining relative importance and effective settings for genetic algorithm control parameters. Evolutionary Computation, 23(2), 309-342.
  • Popov, A., (2005). Genetic algorithms for optimization, TU.
  • Örnek, O., & Ertaş, H. A., (2020). Sıralı kontrol; giriş şekillendirme ve PID kontrolü bir araya getiren yeni bir kontrol yöntemi. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 188-196.
  • Price, K., & Storn, R., (1997). Differential evolution: numerical optimization made easy. Dr. Dobb’s Journal, 220(1), 18–24.
  • Price, K., (1999). An introduction to differential evolution, McGraw-Hill.
  • Price, K., Storn, R., & Lampinen, J., (2005). Differential evolution: a practical approach to global optimization. Springer-Verlag.
  • Rahmani, R., & Yusof, R., (2014). A new simple fast and efficient algorithm for global optimization over continuous search-space problems: radial movement optimization. Applied Mathematics and Computation, 248(1), 287-300.
  • Rahmani, R., Yusof, R., & Ismail, N., (2015). A new metaheuristic algorithm for global optimization over continuous search space. ICIC Express Letters, 9(5), 1335-1340.
  • Seyedmahmoudian, M., Soon, T., Horan, B., Ghandhari, A., Mekhilef, S., & Stojcevski, A. (2019). New ARMO-based MPPT technique to minimize tracking time and fluctuation at output of PV systems under rapidly changing shading conditions. IEEE Transactions On Industrial Informatics, 1-1.
  • Sethi, D., & Singhal, A. (2017). Comparative analysis of a recommender system based on ant colony optimization and artificial bee colony optimization algorithms. 8th International Conference on Computing Communication and Networking Technologies, India, 1-4.
  • Tabak, A., (2020). Fırçasız doğru akım motorlarının hız kontrolünü gerçekleştirmek için PID/PD kontrolcü tasarımı ve performans incelemesi. Avrupa Bilim ve Teknoloji Dergisi, (19), 145-155.
  • Taşören, A. E., Gökçen, A., Soydemir, M. U., & Şahin, S., (2020). Artificial neural network-based adaptive PID controller design for vertical takeoff and landing model. European Journal of Science and Technology, (Special Issue), 87-93.
  • Thangaraj, R., Pant, M., Bouvry, P., & Abraham, A., (2011). Solving stochastic programming problems using modified differential evolution algorithms. Logic Journal of IGPL, 20(4), 732-746.
  • Trebi-Ollennu, A., & White, B., (1997). Multiobjective fuzzy genetic algorithm optimisation approach to nonlinear control system design. IEE Proceedings Control Theory and Applications, 144(2), 137-142.
  • Turkmen, I., & Guney, K., (2004). Artificial neural networks for calculating the association probabilities in multi-target tracking. IEE Proceedings Radar Sonar and Navigation, 151(4), 181-188.
  • Vanithasri, M., Balamurugan, R., & Lakshminarasimman, L., (2018). Radial movement optimization (RMO) technique for solving unit commitment problem in power systems. Journal Of Electrical Systems And Information Technology, 5(3), 697-707.
  • Zhang, R., & Wu, C., (2012). A neighbourhood property for the job shop scheduling problem with application to hybrid particle swarm optimization. IMA Journal of Management Mathematics, 24(1), 111-134.
  • Zhang, X., & Wang, L., (2018). Antenna design by an adaptive variable differential artificial bee colony algorithm. IEEE Transactions on Magnetics, 54(3), 1-4.

Oransal İntegral Türevsel Denetleyici Parametrelerinin Sezgisel Optimizasyon Yöntemleri ile Ayarlanması

Year 2021, Issue: 23, 9 - 21, 30.04.2021
https://doi.org/10.31590/ejosat.830467

Abstract

Günümüzde teknolojinin gelişimine paralel olarak otomatik kontrol sistemlerine olan ihtiyaç artmıştır. Bu sistemler üretim süreçlerinin hızlarını, doğruluklarını ve güvenilirliklerini arttırmakla beraber maliyetlerini de düşürmüşlerdir. Literatürde birçok oransal denetim yöntemi mevcut olup, bunlar içerisinde en yaygın olarak kullanılanı oransal integral türevsel (OİT) kontroldür. OİT kontrol hata tabanlı çalıştığı için denetlenen sistemin matematiksel modelinin bilinmesine gerek yoktur. Bu yöntemde oransal bileşen hatanın genliğine, integral kısmı hatanın alanına ve türevsel parça ise hatanın eğimine göre üretilen kontrol sinyaline katkı sağlamaktadır. İntegral eylemi hatanın geçmişini, oransal etki şimdiki değerini ve türevsel bileşen de geleceğini referans almaktadır. Bu açıdan OİT denetim hatanın geçmişi, anlık değeri ve geleceğine göre uygun kontrol sinyallerini üretmektedir. OİT kontrolde bu üç bileşenin sistem üzerindeki etkisi kazanç sabitleri ile belirlenmektedir. Denetleyicinin başarımı kazanç sabitlerine oldukça bağlıdır. Uygun seçilmiş kazanç sabitleri ile başarılı kontrol süreçlerini gerçekleştirirken, parametrelerinin yanlış seçimi ise sistemi kararsız hale getirebilmektedir.
Bu makalede, radyal hareket optimizasyonunun (RHO) yeni bir varyantı olarak değiştirilmiş radyal hareket optimizasyonu (DRHO) önerilmiştir. Ayrıca seçilen üç test sistem için OİT denetleyicinin kazanç faktörleri, önerilen yöntem (DRHO), parçacık sürüsü optimizasyonu (PSO), radyal hareket optimizasyonu (RHO), farksal gelişim (FG) ve genetik algoritma (GA) ile en iyileştirilmiştir. Sezgisel optimizasyon yöntemleri ile parametre ayarında mutlak hata toplamı, hata karesi toplamı, zaman ağırlıklı mutlak hata toplamı ve zaman ağırlıklı hata karesi toplamı olmak üzere dört farklı hata alanı tabanlı başarım kriteri kullanılmıştır. Öncelikle bu beş sezgisel optimizasyon yöntemi (DRHO, PSO, RHO, FG ve GA) destekli OİT denetleyicinin başarımı seçilen hata alanı kriterine göre kıyaslanmış ve ardından optimizasyon yöntemlerinin performansları birbirleri ile karşılaştırılmış ve elde edilen sonuçlar analiz edilmiştir. Literatürde OİT denetleyici parametrelerinin PSO, FG, GA ve RHO kullanılarak ayarlanmasına yönelik çok sayıda çalışma mevcuttur. Ancak bildiğimiz kadarıyla OİT denetleyicinin kazanç değerlerinin önerilen DRHO ile en iyileştirilmesi konulu bir çalışma bulunmamaktadır. Bu durum sunulan bu çalışmanın mevcut literatüre en önemli katkısıdır.

References

  • Boudardara, F., & Gorkemli, B. (2018). Application of artificial bee colony programming to two trails of the artificial ant problem. 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, Turkey, 1-6.
  • Çeven, S., & Albayrak, A. (2020). Çift ters sarkaç sisteminin kontrolü için PID ve LQR kontrolcü tasarımlarının modellenmesi. Avrupa Bilim ve Teknoloji Dergisi, (Special Issue), 323-330.
  • Deb, K., (1999). An introduction to genetic algorithms. Sadhana, 24(4), 293-315.
  • Denizci, A., & Ulu, C. (2020). Fuzzy cognitive map based PID controller design. Avrupa Bilim ve Teknoloji Dergisi, (Special Issue), 165-171.
  • Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Japan, 39-43.
  • Eberhart, R. C., Simpson, P. K., & Dobbins, R. W., (1996). Computational intelligence PC tools. Academic Press.
  • Fokas, A., (2002). A new transform method for evolution partial differential equations. IMA Journal of Applied Mathematics, 67(6), 559-590.
  • Gille, J., & Paquet, J., (1962). Subharmonic oscillations in on-off control systems. Transactions of the American Institute of Electrical Engineers Part II: Applications and Industry, 81(4), 210-216.
  • Gür, H., & Furat, M., (2020). Özelleştirilmiş uygunluk fonksiyonu tabanlı su döngüsü algoritması ile PID parametrelerinin optimizasyonu. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 332-341.
  • Hägglund, T., & Åström, K. J., (2004). Revisiting the Ziegler-Nichols step response method for PID control. Journal of Process Control, 14(6), 635–650.
  • Heppner, H., & Grenander, U., (1990). A stochastic non-linear model for coordinated bird flocks. AAAS.
  • Jing, H., Liu, Z., & Chen, H., (2011). A switched control strategy for antilock braking system with on/off valves. IEEE Transactions on Vehicular Technology, 60(4), 1470-1484.
  • Jin, L., & Feng, Q., (2018). Improved radial movement optimization to determine the critical failure surface for slope stability analysis. Environmental Earth Sciences, 77(16). 564-576.
  • Jin, L., Zhang, H., & Feng, Q. (2019). Application of improved radial movement optimization for calculating the upper bound of ultimate bearing capacity of shallow foundation on unsaturated soil. Computers And Geotechnics, 109, 82-88.
  • Kaya, R., & Furat, M., (2020). Three-channel cost function based artificial bee colony algorithm for PID tuning. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 382-392.
  • Kennedy, J., & Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. In Proceedings of the Conference on Systems Man and Cybernetics, USA, 4104–4109.
  • Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization, IV IEEE International Conference on Neural Networks, USA, 1942–1948.
  • Koçer, B., (2017). İstatistiksel olarak yönlendirilen yapay arı kolonisi algoritması. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 5(2), 153-169.
  • Koswara, A., & Nagy, Z., (2017). ON–OFF feedback control of plug-flow crystallization: A Case of Quality-by-Control in Continuous Manufacturing. IEEE Life Sciences Letters, 3(1), 1-4.
  • Köse, E. & Coşkun, S., (2020). Time-delay AVR system analysis using PSO-based PID controller. European Journal of Science and Technology, 18, 981-991.
  • Köse, O. & Oktay, T. (2020). Investigation of the effect of differential morphing on lateral flight by using PID algorithm in quadrotors. Avrupa Bilim ve Teknoloji Dergisi, 18, 636-644.
  • Lee, T., (2008). Optimal wind–battery coordination in a power system using evolutionary iteration particle swarm optimisation. IET Generation Transmission & Distribution, 2(2), 291-300.
  • Mills, K., Filliben, J., & Haines, A., (2015). Determining relative importance and effective settings for genetic algorithm control parameters. Evolutionary Computation, 23(2), 309-342.
  • Popov, A., (2005). Genetic algorithms for optimization, TU.
  • Örnek, O., & Ertaş, H. A., (2020). Sıralı kontrol; giriş şekillendirme ve PID kontrolü bir araya getiren yeni bir kontrol yöntemi. Avrupa Bilim ve Teknoloji Dergisi, (Özel Sayı), 188-196.
  • Price, K., & Storn, R., (1997). Differential evolution: numerical optimization made easy. Dr. Dobb’s Journal, 220(1), 18–24.
  • Price, K., (1999). An introduction to differential evolution, McGraw-Hill.
  • Price, K., Storn, R., & Lampinen, J., (2005). Differential evolution: a practical approach to global optimization. Springer-Verlag.
  • Rahmani, R., & Yusof, R., (2014). A new simple fast and efficient algorithm for global optimization over continuous search-space problems: radial movement optimization. Applied Mathematics and Computation, 248(1), 287-300.
  • Rahmani, R., Yusof, R., & Ismail, N., (2015). A new metaheuristic algorithm for global optimization over continuous search space. ICIC Express Letters, 9(5), 1335-1340.
  • Seyedmahmoudian, M., Soon, T., Horan, B., Ghandhari, A., Mekhilef, S., & Stojcevski, A. (2019). New ARMO-based MPPT technique to minimize tracking time and fluctuation at output of PV systems under rapidly changing shading conditions. IEEE Transactions On Industrial Informatics, 1-1.
  • Sethi, D., & Singhal, A. (2017). Comparative analysis of a recommender system based on ant colony optimization and artificial bee colony optimization algorithms. 8th International Conference on Computing Communication and Networking Technologies, India, 1-4.
  • Tabak, A., (2020). Fırçasız doğru akım motorlarının hız kontrolünü gerçekleştirmek için PID/PD kontrolcü tasarımı ve performans incelemesi. Avrupa Bilim ve Teknoloji Dergisi, (19), 145-155.
  • Taşören, A. E., Gökçen, A., Soydemir, M. U., & Şahin, S., (2020). Artificial neural network-based adaptive PID controller design for vertical takeoff and landing model. European Journal of Science and Technology, (Special Issue), 87-93.
  • Thangaraj, R., Pant, M., Bouvry, P., & Abraham, A., (2011). Solving stochastic programming problems using modified differential evolution algorithms. Logic Journal of IGPL, 20(4), 732-746.
  • Trebi-Ollennu, A., & White, B., (1997). Multiobjective fuzzy genetic algorithm optimisation approach to nonlinear control system design. IEE Proceedings Control Theory and Applications, 144(2), 137-142.
  • Turkmen, I., & Guney, K., (2004). Artificial neural networks for calculating the association probabilities in multi-target tracking. IEE Proceedings Radar Sonar and Navigation, 151(4), 181-188.
  • Vanithasri, M., Balamurugan, R., & Lakshminarasimman, L., (2018). Radial movement optimization (RMO) technique for solving unit commitment problem in power systems. Journal Of Electrical Systems And Information Technology, 5(3), 697-707.
  • Zhang, R., & Wu, C., (2012). A neighbourhood property for the job shop scheduling problem with application to hybrid particle swarm optimization. IMA Journal of Management Mathematics, 24(1), 111-134.
  • Zhang, X., & Wang, L., (2018). Antenna design by an adaptive variable differential artificial bee colony algorithm. IEEE Transactions on Magnetics, 54(3), 1-4.
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Oğuzhan Çakır 0000-0002-6576-8710

Sinan Tekin 0000-0001-9431-2766

Publication Date April 30, 2021
Published in Issue Year 2021 Issue: 23

Cite

APA Çakır, O., & Tekin, S. (2021). Oransal İntegral Türevsel Denetleyici Parametrelerinin Sezgisel Optimizasyon Yöntemleri ile Ayarlanması. Avrupa Bilim Ve Teknoloji Dergisi(23), 9-21. https://doi.org/10.31590/ejosat.830467