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Bulanık Bilişsel Harita Tabanlı PID Kontrolör Tasarımı

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 165 - 171, 15.08.2020
https://doi.org/10.31590/ejosat.779605

Öz

Doğrusal oransal-integral-türev (PID) denetleyiciler sahip oldukları basit yapıları ve etkin performansları nedeniyle endüstriyel uygulamalarda en yaygın biçimde kullanılan denetleyicilerdir. Fakat, endüstriyel sistemlerin doğrusal olmayan karakteristikleri ya da sistem dereceleri arttıkça bu denetleyicilerin performansları düşmektedir. Bu nedenle, doğrusal PID denetleyicilerin performansını iyileştirmek için literatürde çeşitli doğrusal olmayan PID denetleyici modelleri önerilmiştir. Bu çalışmada bulanık bilişsel harita (BBH) tabanlı yeni bir doğrusal olmayan PID denetleyici tasarım yaklaşımı önerilmiştir. İki farklı BBH tabanlı PID denetleyici modeli sunulmuştur. İlk denetleyici modeli, hata, hatanın türevi ve hatanın integralinden oluşan üç girişli klasik paralel PID yapısında bir modeldir. İkinci model ise hata ve hatanın türevi olmak üzere iki girişten oluşan klasik bulanık PID yapısında bir kontrolör modelidir. Önerilen yöntemde, ilk olarak her bir giriş sinyali üyelik fonksiyonları kullanılarak bulanıklaştırılmaktadır. Daha sonra girişler ile çıkış arasındaki nedensel ilişkiler ağırlık parametreleri kullanılarak belirlenmektedir. Son olarak, bir aktivasyon fonksiyonu kullanılarak BBH çıkarımı gerçekleştirilmektedir. Bu nedenle, önerilen doğrusal olmayan PID modellerinde sırasıyla dört ve altı ayar parametresi bulunmaktadır. Önerilen BBH tabanlı PID denetleyici modellerinin performanslarını değerlendirmek için dördüncü dereceden doğrusal bir sistem üzerinde benzetim çalışmaları gerçekleştirilmiştir. Bu modellerin performanları, klasik PID denetleyici ve bulanık PID denetleyici performansları ile karşılaştırılmıştır. Karşılaştırma sonuçları, önerilen BBH tabanlı PID denetleyici modellerinin klasik PID ve bulanık PID denetleyicilerden daha üstün bir performans sağladığını göstermektedir.

Kaynakça

  • McMillan, G. (2012). Industrial Applications of PID Control. PID Control In The Third Millennium, 415-461.
  • Mukhtar, A., Tayal, V., & Singh, H. (2019). PSO Optimized PID Controller Design for the Process Liquid Level Control. 2019 3Rd International Conference On Recent Developments In Control, Automation & Power Engineering (RDCAPE).
  • Bélai, I., Huba, M., Burn, K., & Cox, C. (2019). PID and filtered PID control design with application to a positional servo drive. Kybernetika, 540-560.
  • Patra, A., Mishra, A., Nanda, A., Subudhi, D., Agrawal, R., & Patra, A. (2020). Stabilizing and Trajectory Tracking of Inverted Pendulum Based on Fractional Order PID Control. Lecture Notes In Networks And Systems, 338-346.
  • Zaidner, G., Korotkin, S., Shteimberg, E., Ellenbogen, A., Arad, M., & Cohen, Y. (2010). Non linear PID and its application in process control. IEEE 26-Th Convention Of Electrical And Electronics Engineers In Israel, 000574-000577.
  • Quinonez, K., Camacho, O., & Chavez, D. (2019). Application of Nonlinear PID Controllers to Bioreactor Processes. 2019 IEEE 4Th Colombian Conference On Automatic Control (CCAC).
  • Salamat, B., & Tonello, A. (2019). Adaptive Nonlinear PID Control for a Quadrotor UAV Using Particle Swarm Optimization. 2019 IEEE Aerospace Conference.
  • Najm, A., & Ibraheem, I. (2019). Nonlinear PID controller design for a 6-DOF UAV quadrotor system. Engineering Science And Technology, An International Journal, 22(4), 1087-1097.
  • Carvajal, J., Chen, G., & Ogmen, H. (2000). Fuzzy PID controller: Design, performance evaluation, and stability analysis. Information Sciences, 123(3-4), 249-270.
  • Somwanshi, D., Bundele, M., Kumar, G., & Parashar, G. (2019). Comparison of Fuzzy-PID and PID Controller for Speed Control of DC Motor using LabVIEW. Procedia Computer Science, 152, 252-260.
  • Kosko, B. (1986). Fuzzy cognitive maps. International Journal Of Man-Machine Studies, 24(1), 65-75.
  • Papageorgiou, E., & Salmeron, J. (2013). A Review of Fuzzy Cognitive Maps Research During the Last Decade. IEEE Transactions On Fuzzy Systems, 21(1), 66-79.
  • Salmeron, J. (2009). Supporting Decision Makers with Fuzzy Cognitive Maps. Research-Technology Management, 52(3), 53-59.
  • Arruda, L., Mendonca, M., Neves, F., Chrun, I., & Papageorgiou, E. (2018). Artificial Life Environment Modeled by Dynamic Fuzzy Cognitive Maps. IEEE Transactions On Cognitive And Developmental Systems, 10(1), 88-101.
  • Najafi, A., Amirkhani, A., Papageorgiou, E., & Mosavi, M. (2017). Medical decision making based on fuzzy cognitive map and a generalization linguistic weighted power mean for computing with words. 2017 IEEE International Conference On Fuzzy Systems (FUZZ-IEEE), 1-6.
  • YAMAN, D., & POLAT, S. (2009). A fuzzy cognitive map approach for effect-based operations: An illustrative case. Information Sciences, 179(4), 382-403.
  • Hajek, P., & Prochazka, O. (2016). Interval-valued fuzzy cognitive maps for supporting business decisions. 2016 IEEE International Conference On Fuzzy Systems (FUZZ-IEEE), 531-536.
  • Groumpos, P., & Stylios, C. (2000). Modelling supervisory control systems using fuzzy cognitive maps. Chaos, Solitons & Fractals, 11(1-3), 329-336.
  • Stylios, C., & Groumpos, P. (2004). Modeling Complex Systems Using Fuzzy Cognitive Maps. IEEE Transactions On Systems, Man, And Cybernetics - Part A: Systems And Humans, 34(1), 155-162.
  • Stylios, C., & Groumpos, P. (1999). Fuzzy Cognitive Maps: a model for intelligent supervisory control systems. Computers In Industry, 39(3), 229-238.
  • Farinaz, B., Abdul, R., Khairulmizam, S., & Hossein, E. (2017). Energy saving by applying the fuzzy cognitive map control in controlling the temperature and humidity of room. International Journal Of Physical Sciences, 12(1), 13-23.
  • Mendonça, M., Neves, F., de Arruda, L., Chrun, I., & Papageorgiou, E. (2016). Embedded Dynamic Fuzzy Cognitive Maps for Controller in Industrial Mixer. Intelligent Decision Technologies 2016, 57, 251-261.
  • de Souza, L., Prieto Soares, P., Mendonca, M., Mourhir, A., & Papageorgiou, E. (2018). Fuzzy Cognitive Maps and Fuzzy Logic applied in industrial processes control. 2018 IEEE International Conference On Fuzzy Systems (FUZZ-IEEE), 1-8.
  • Amirkhani, A., Shirzadeh, M., Papageorgiou, E., & Mosavi, M. (2016). Visual-based quadrotor control by means of fuzzy cognitive maps. ISA Transactions, 60, 128-142.
  • Denizci, A., Karadeniz, S., & Ulu, C. Fuzzy Cognitive Map Based PI Controller Design. International Conference On Intelligent And Fuzzy Systems (INFUS 2020), in press.
  • Chunchen, W., Feng, C., Guang, Z., Ming, Y., Liangzhe, L., & Taihu, W. (2017). Design of genetic algorithm optimized PID controller for gas mixture system. 2017 13Th IEEE International Conference On Electronic Measurement & Instruments (ICEMI), 6-9.

Fuzzy Cognitive Map Based PID Controller Design

Yıl 2020, Ejosat Özel Sayı 2020 (HORA), 165 - 171, 15.08.2020
https://doi.org/10.31590/ejosat.779605

Öz

Linear proportional-integral-derivative (PID) controllers are the most widely used process controllers in industrial applications due to their simple structures and effective performances. However, performances of these controllers reduce as the nonlinear characteristics or the system orders of the industrial processes increases. Therefore, various nonlinear PID controller models are proposed in literature to improve the control performances of linear PID controllers. In this study, a new nonlinear PID controller design approach is proposed based on the fuzzy cognitive map (FCM) method. Two different FCM based PID controller models are introduced. The first controller model is in the conventional parallel PID structure with three inputs as the error, the derivative of the error, and the integral of the error. On the other hand, the second controller model is in the conventional fuzzy PID form with two inputs as the error and the derivative of the error. In the proposed method, each input signal is firstly fuzzified by using a membership function. Then, causal relationships between inputs and the output are determined by using weight parameters. Finally, the FCM inference is performed by using an activation function. Therefore, the proposed nonlinear PID controllers have four and six tuning parameters, respectively. Simulation studies are performed on a fourth order linear system in order to evaluate the performance of the proposed FCM based PID controller models. The performances of these controller models are compared with a conventional PID controller and a fuzzy PID controller. The comparison results show that the proposed FCM based PID controller models outperform the conventional PID and fuzzy PID controllers.

Kaynakça

  • McMillan, G. (2012). Industrial Applications of PID Control. PID Control In The Third Millennium, 415-461.
  • Mukhtar, A., Tayal, V., & Singh, H. (2019). PSO Optimized PID Controller Design for the Process Liquid Level Control. 2019 3Rd International Conference On Recent Developments In Control, Automation & Power Engineering (RDCAPE).
  • Bélai, I., Huba, M., Burn, K., & Cox, C. (2019). PID and filtered PID control design with application to a positional servo drive. Kybernetika, 540-560.
  • Patra, A., Mishra, A., Nanda, A., Subudhi, D., Agrawal, R., & Patra, A. (2020). Stabilizing and Trajectory Tracking of Inverted Pendulum Based on Fractional Order PID Control. Lecture Notes In Networks And Systems, 338-346.
  • Zaidner, G., Korotkin, S., Shteimberg, E., Ellenbogen, A., Arad, M., & Cohen, Y. (2010). Non linear PID and its application in process control. IEEE 26-Th Convention Of Electrical And Electronics Engineers In Israel, 000574-000577.
  • Quinonez, K., Camacho, O., & Chavez, D. (2019). Application of Nonlinear PID Controllers to Bioreactor Processes. 2019 IEEE 4Th Colombian Conference On Automatic Control (CCAC).
  • Salamat, B., & Tonello, A. (2019). Adaptive Nonlinear PID Control for a Quadrotor UAV Using Particle Swarm Optimization. 2019 IEEE Aerospace Conference.
  • Najm, A., & Ibraheem, I. (2019). Nonlinear PID controller design for a 6-DOF UAV quadrotor system. Engineering Science And Technology, An International Journal, 22(4), 1087-1097.
  • Carvajal, J., Chen, G., & Ogmen, H. (2000). Fuzzy PID controller: Design, performance evaluation, and stability analysis. Information Sciences, 123(3-4), 249-270.
  • Somwanshi, D., Bundele, M., Kumar, G., & Parashar, G. (2019). Comparison of Fuzzy-PID and PID Controller for Speed Control of DC Motor using LabVIEW. Procedia Computer Science, 152, 252-260.
  • Kosko, B. (1986). Fuzzy cognitive maps. International Journal Of Man-Machine Studies, 24(1), 65-75.
  • Papageorgiou, E., & Salmeron, J. (2013). A Review of Fuzzy Cognitive Maps Research During the Last Decade. IEEE Transactions On Fuzzy Systems, 21(1), 66-79.
  • Salmeron, J. (2009). Supporting Decision Makers with Fuzzy Cognitive Maps. Research-Technology Management, 52(3), 53-59.
  • Arruda, L., Mendonca, M., Neves, F., Chrun, I., & Papageorgiou, E. (2018). Artificial Life Environment Modeled by Dynamic Fuzzy Cognitive Maps. IEEE Transactions On Cognitive And Developmental Systems, 10(1), 88-101.
  • Najafi, A., Amirkhani, A., Papageorgiou, E., & Mosavi, M. (2017). Medical decision making based on fuzzy cognitive map and a generalization linguistic weighted power mean for computing with words. 2017 IEEE International Conference On Fuzzy Systems (FUZZ-IEEE), 1-6.
  • YAMAN, D., & POLAT, S. (2009). A fuzzy cognitive map approach for effect-based operations: An illustrative case. Information Sciences, 179(4), 382-403.
  • Hajek, P., & Prochazka, O. (2016). Interval-valued fuzzy cognitive maps for supporting business decisions. 2016 IEEE International Conference On Fuzzy Systems (FUZZ-IEEE), 531-536.
  • Groumpos, P., & Stylios, C. (2000). Modelling supervisory control systems using fuzzy cognitive maps. Chaos, Solitons & Fractals, 11(1-3), 329-336.
  • Stylios, C., & Groumpos, P. (2004). Modeling Complex Systems Using Fuzzy Cognitive Maps. IEEE Transactions On Systems, Man, And Cybernetics - Part A: Systems And Humans, 34(1), 155-162.
  • Stylios, C., & Groumpos, P. (1999). Fuzzy Cognitive Maps: a model for intelligent supervisory control systems. Computers In Industry, 39(3), 229-238.
  • Farinaz, B., Abdul, R., Khairulmizam, S., & Hossein, E. (2017). Energy saving by applying the fuzzy cognitive map control in controlling the temperature and humidity of room. International Journal Of Physical Sciences, 12(1), 13-23.
  • Mendonça, M., Neves, F., de Arruda, L., Chrun, I., & Papageorgiou, E. (2016). Embedded Dynamic Fuzzy Cognitive Maps for Controller in Industrial Mixer. Intelligent Decision Technologies 2016, 57, 251-261.
  • de Souza, L., Prieto Soares, P., Mendonca, M., Mourhir, A., & Papageorgiou, E. (2018). Fuzzy Cognitive Maps and Fuzzy Logic applied in industrial processes control. 2018 IEEE International Conference On Fuzzy Systems (FUZZ-IEEE), 1-8.
  • Amirkhani, A., Shirzadeh, M., Papageorgiou, E., & Mosavi, M. (2016). Visual-based quadrotor control by means of fuzzy cognitive maps. ISA Transactions, 60, 128-142.
  • Denizci, A., Karadeniz, S., & Ulu, C. Fuzzy Cognitive Map Based PI Controller Design. International Conference On Intelligent And Fuzzy Systems (INFUS 2020), in press.
  • Chunchen, W., Feng, C., Guang, Z., Ming, Y., Liangzhe, L., & Taihu, W. (2017). Design of genetic algorithm optimized PID controller for gas mixture system. 2017 13Th IEEE International Conference On Electronic Measurement & Instruments (ICEMI), 6-9.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Aykut Denizci Bu kişi benim 0000-0002-9307-4417

Cenk Ulu 0000-0002-8588-6247

Yayımlanma Tarihi 15 Ağustos 2020
Yayımlandığı Sayı Yıl 2020 Ejosat Özel Sayı 2020 (HORA)

Kaynak Göster

APA Denizci, A., & Ulu, C. (2020). Fuzzy Cognitive Map Based PID Controller Design. Avrupa Bilim Ve Teknoloji Dergisi165-171. https://doi.org/10.31590/ejosat.779605