Research Article
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Year 2020, Volume: 8 Issue: 5, 25 - 34, 29.12.2020
https://doi.org/10.21923/jesd.826518

Abstract

There are increasing studies for precise control applications related to pneumatic systems, which are widely used such as automation, robotics and hospitals. However, nonlinearity and complexity of the system make it difficult to control the system. Mathematical models are used to determine the behavior of the system and to express outputs according to given inputs. The aim is to control the system and to predict its behavior.
There are many control theories for linear systems, but these theories are few for nonlinear systems. Thus, nonlinear systems are more easily controlled. Thanks to the pneumatic systems used in the hospitals, a great saving in time and energy are ensured between the rooms and the laboratory. Hospital pneumatic systems have a nonlinear structure and they are modeled with fuzzy logic to control the system in the most efficient way. Pneumatic systems that are modeled with fuzzy logic can be used more effectively, solutions to the problems can be found more easily and system efficiency can be reached to the highest level. Modeled with fuzzy logic using 4 inputs and 1 output parameters, the success of the system is approximately 90% (91.6%).

References

  • Akkaya, Ali Volkan, et al, 2011. Simulink kullanarak bir pnömatik sistemin simülasyonu. Doğuş Üniversitesi Dergisi ,6.2 (s.155-162).
  • Barbosa, Paulo Roberto, and Paulo Seleghim Jr.,2011. On the Application of Fuzzy Logic Control in Pneumatic Conveying systems. Journal of the Brazilian Neural Network Society, 9.4, 256-265.
  • Frigerio, N., Matta, A., 2015. Energy-Efficient Control Strategies for Machine Tools With Stochastic Arrivals. IEEE Transactions on Automation Science and Engineering.
  • Kiliçkan, A., Güner, M. ,2006. Pneumatic conveying characteristics of cotton seeds. Biosystems Engineering, 95.4: 537-546.
  • Kumbla, Kishan Kumar, and Mo Jamshidi, 1994. Control of robotic manipulator using fuzzy logic. In Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, pp. 518-523. IEEE.
  • Kuzucu, Et Al. ,2015. Pnömatik Silindirlerde Basınç Geri Beslemesi ile Hassas Konum Kontrolü.
  • Kuzucu, Karaca, Benlıgırayoglu, 2003. Pnömatik Silindirlerde Basınç Geri Beslemesi İle Hassas Konum Kontrolü. III. Ulusal Hidrolik Pnömatik Kongresi Ve Sergisi.
  • Mamdani, E. H., 1974. Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 121(12).
  • Marumo, R. Tokhi, O. M., 2004. Intelligent Modeling and Control of a Pneumatic Motor, IEEE.
  • Saidur, R., Rahim, N.A., Hasanuzzaman, M., 2010. A review on compressed air energy use and energy savings, Renewable and Sustainable Energy Reviews 14, p.1135-1153.
  • Shi, L., Sepehri, N., 2004. Adaptive Fuzzy-Neural-Based Multiple Models for Fault Diagnosis of a Pneumatic Actuator. American Control Conference Boston, Massachusetts.
  • Um, T., Joo, Y., Kong, Y., Chun, I., Kim, S., & Bang, J., 2003. Optimization of design parameters of a pneumatic system for solid freeform fabrication system using genetic algorithm. In Proceedings of 2003 IEEE Conference on Control Applications, Vol. 1, pp. 120-123. IEEE.
  • Wang, J.; PU, J.; Moore, P. R.; Zhang, Z., 1998. Modeling study and servo-control of air motor systems. Int. J. Control, Vol.71, No. 3, 459-476.
  • Yager, R. R., & Filev, D. P., 1994. Essentials of fuzzy modeling and control. New York, 388, 22-23.
  • Yang, Gang, et al., 2017. Asymmetric fuzzy control of a positive and negative pneumatic pressure servo system. Chinese Journal of Mechanical Engineering, 30.6 ,1438-1446.
  • Zaim A., H. Aras, 2020. Pnömatik Sistemlerde Enerji Verimliliği. Engineer and Machinery, vol. 61, no. 698, p. 31-45, Review Article.

HASTANE PNÖMATİK SİSTEMLERİNİN BULANIK MANTIKLA MODELLENMESİ

Year 2020, Volume: 8 Issue: 5, 25 - 34, 29.12.2020
https://doi.org/10.21923/jesd.826518

Abstract

Otomasyon, robotik ve hastaneler gibi oldukça geniş kullanım alanı olan; Pnömatik sistemlerle ilgili hassas kontrol uygulamalarına yönelik çalışmalar giderek artmaktadır. Ancak sistemin lineer olmayan ve karmaşık bir yapıya sahip olması, sistemin kontrol edilmesini zorlaştırmaktadır. Sistemin davranışını belirlemek ve verilen girişlere göre çıkışların ifade edilmesi için matematiksel modeller kullanılmaktadır. Amaç sistemin davranışının öngörülmesi ve kontrolüdür.
Doğrusal sistemler için birçok kontrol teorisi varken, doğrusal olmayan sistemlerde kontrol teorileri kısıtlıdır. Doğrusal olarak modellenemeyen sistemler bulanık olarak modellenebilir. Böylece lineer olmayan sistemlerin kontrolleri daha kolay bir şekilde sağlanmaktadır. Hastanelerde kullanılan pnömatik sistemler sayesinde numune alınan odalar ile laboratuvar arasında zaman ve enerji açısından büyük bir tasarruf sağlar. Hastane pnömatik sistemleri de lineer olmayan bir yapıya sahiptir ve bulanık mantıkla modellenerek sistemin en verimli şekilde kontrolü amaçlanmaktadır. Bu sayede hastane pnömatik sistemleri, daha etkili bir şekilde kullanılabilecek, karşılaşılan problemlere daha kolay bir şekilde çözüm bulunabilecek ve sistem verimliliği en üst seviyeye ulaşabilecektir. 4 giriş ve 1 çıkış parametresi kullanılarak bulanık mantıkla modellenen sistemin başarısı yaklaşık olarak %90 (%91,6) dır.

References

  • Akkaya, Ali Volkan, et al, 2011. Simulink kullanarak bir pnömatik sistemin simülasyonu. Doğuş Üniversitesi Dergisi ,6.2 (s.155-162).
  • Barbosa, Paulo Roberto, and Paulo Seleghim Jr.,2011. On the Application of Fuzzy Logic Control in Pneumatic Conveying systems. Journal of the Brazilian Neural Network Society, 9.4, 256-265.
  • Frigerio, N., Matta, A., 2015. Energy-Efficient Control Strategies for Machine Tools With Stochastic Arrivals. IEEE Transactions on Automation Science and Engineering.
  • Kiliçkan, A., Güner, M. ,2006. Pneumatic conveying characteristics of cotton seeds. Biosystems Engineering, 95.4: 537-546.
  • Kumbla, Kishan Kumar, and Mo Jamshidi, 1994. Control of robotic manipulator using fuzzy logic. In Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, pp. 518-523. IEEE.
  • Kuzucu, Et Al. ,2015. Pnömatik Silindirlerde Basınç Geri Beslemesi ile Hassas Konum Kontrolü.
  • Kuzucu, Karaca, Benlıgırayoglu, 2003. Pnömatik Silindirlerde Basınç Geri Beslemesi İle Hassas Konum Kontrolü. III. Ulusal Hidrolik Pnömatik Kongresi Ve Sergisi.
  • Mamdani, E. H., 1974. Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 121(12).
  • Marumo, R. Tokhi, O. M., 2004. Intelligent Modeling and Control of a Pneumatic Motor, IEEE.
  • Saidur, R., Rahim, N.A., Hasanuzzaman, M., 2010. A review on compressed air energy use and energy savings, Renewable and Sustainable Energy Reviews 14, p.1135-1153.
  • Shi, L., Sepehri, N., 2004. Adaptive Fuzzy-Neural-Based Multiple Models for Fault Diagnosis of a Pneumatic Actuator. American Control Conference Boston, Massachusetts.
  • Um, T., Joo, Y., Kong, Y., Chun, I., Kim, S., & Bang, J., 2003. Optimization of design parameters of a pneumatic system for solid freeform fabrication system using genetic algorithm. In Proceedings of 2003 IEEE Conference on Control Applications, Vol. 1, pp. 120-123. IEEE.
  • Wang, J.; PU, J.; Moore, P. R.; Zhang, Z., 1998. Modeling study and servo-control of air motor systems. Int. J. Control, Vol.71, No. 3, 459-476.
  • Yager, R. R., & Filev, D. P., 1994. Essentials of fuzzy modeling and control. New York, 388, 22-23.
  • Yang, Gang, et al., 2017. Asymmetric fuzzy control of a positive and negative pneumatic pressure servo system. Chinese Journal of Mechanical Engineering, 30.6 ,1438-1446.
  • Zaim A., H. Aras, 2020. Pnömatik Sistemlerde Enerji Verimliliği. Engineer and Machinery, vol. 61, no. 698, p. 31-45, Review Article.
There are 16 citations in total.

Details

Primary Language Turkish
Journal Section Research Articles
Authors

Büşra Takgil This is me 0000-0002-7927-0083

Resul Kara 0000-0001-8902-6837

Publication Date December 29, 2020
Submission Date November 16, 2020
Acceptance Date December 24, 2020
Published in Issue Year 2020 Volume: 8 Issue: 5

Cite

APA Takgil, B., & Kara, R. (2020). HASTANE PNÖMATİK SİSTEMLERİNİN BULANIK MANTIKLA MODELLENMESİ. Mühendislik Bilimleri Ve Tasarım Dergisi, 8(5), 25-34. https://doi.org/10.21923/jesd.826518