Improving End-Point Position Control in Hydraulic Testing Machines with a Fuzzy Logic Based Approach

Year 2023, Volume: 9 Issue: 3, 531 - 544, 20.09.2023

Serkan Anlak , Ekrem Düven

https://doi.org/10.28979/jarnas.1283735

Abstract

During the repetitive operation of hydraulic testing machines, some undesirable vibration movements and non-compliance with the set value may occur at the piston end-point, which is the output of the system. PID (Proportional-Integral-Derivative) control is widely used in such systems in practical applications. However, the use of a standard (fixed coefficient) PID control alone cannot completely eliminate problems such as endpoint vibration and/or non-compliance of the endpoint position with the set value, caused by dynamic parameter changes in the hydraulic system. In the current state of the applications, when such a situation is encountered, the controller coefficients need to be readjusted by a human operator. In this study, to avoid this need and automatically adjust PID controller coefficients, a fuzzy logic-based computation approach has been developed and applied to the existing control system. A hydraulic system was designed and realized to test the developed method. The end-point position control of the system was established and improved utilizing the developed approach. With this development, an improvement of more than 10% was achieved in the adjustment of the hydraulic testing machine end-point oscillation amplitude to the set value. The use of this method also eliminates the need for human operators to readjust the controller parameters in case of long-term operation of hydraulic test systems.

Keywords

Hydraulic testing machines, Fuzzy Logic, Industrial control, PID control, repetitive tasks

References

• Ak, A., Yılmaz, E. & Katrancıoglu, S. (2023). Integral Fuzzy Sliding Mode Controller for Hydraulic System Using Neural Network Modelling. Gazi University Journal of Science, 36(3). DOI: https://doi.org/10.35378/gujs.979370
• Aydoğdu, Ö. & Çatkafa, A. (2019). Bir hidrolik derin çekme pres makinesinin PLC tabanlı bulanık mantık kontrolü ve endüstri 4.0 uygulaması. Konya Journal of Engineering Sciences, 7(3), 573-584. DOI: https://doi.org/10.36306/konjes.613867
• Çınar, E. (2013). Position control of hydraulic cylinder with fuzzy logic method (In Turkish), (Master’s thesis), Graduate School of Natural and Appl. Sciences, Gazi University, Ankara, Turkey.
• Çınar, E., Ulaş, H. B. & Bilgin, M. (2014). Hidrolik silindirin bulanık mantık yöntemi ile konum kontrolü. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, 30(3), 214-229. Retrieved from: https://dergipark.org.tr/en/pub/erciyesfen/issue/25557/269587
• Demirel, K. (2016). Hydraulic and pneumatic (In Turkish), Birsen Yayınevi, İstanbul, Turkey.
• Jian, M. Z., Sheng, D. Z. & Shu, G. W. (2009). Application of self-tuning fuzzy PID controller for a SRM direct drive volume control hydraulic press. Control Engineering Practice, 17(12), 1398–1404. DOI: https://doi.org/10.1016/j.conengprac.2009.07.001
• Jones, E., Dopson, A. & Roskilly, A. P. (2000). Design of a reduced-rule self-organizing fuzzy logic controller for water hydraulic applications. Proceedings of the Institution of Mechanical Engineers, 214(5), 371-381. DOI: https://doi.org/10.1243/0959651001540726
• Kovari, A. (2009). Mathematical model and simulation of electrohydraulic servo systems. Dunaújváros University Multi-Interdisciplinary Conference Series, Hungary, [2009, Nov.9-13]. DOI: https://doi.org/10.13140/2.1.4875.8564
• Özdemir, C., Öztürk, S., Şengül, Ö. & Kuncan, F. (2022). Position control of the suspended pendulum system with particle swarm optimization algorithm. El-Cezerî Journal of Science and Engineering, 9(2), 669-679. DOI: https://doi.org/10.31202/ecjse.993313
• Üçüncü, K. (2020). Hidrolik ve pnömatik sistemler ders notları, Faculty of Engineering, Karadeniz Technical University, Trabzon, Turkey. Retrieved from: https://avesis.ktu.edu.tr/resume/downloadfile/kucuncu?key=ac5bff0e-1ff3-4f4b-a805-420301ff2dbc
• Wang, C., Quan, L., Jiao, Z. & Zhang, S. (2017). Nonlinear adaptive control of hydraulic system with observing and compensating mismatching uncertainties. IEEE Transactions on Control Systems Technology, 26(3): 1-12. DOI: https://doi.org/10.1109/TCST.2017.2699166
• Yılmaz, E. (2012). Modelling a hydraulic system using artificial neural networks and controlling with PID algorithm coefficients optimized by genetic and particle swarm algorithms (In Turkish), (Master’s Thesis), Marmara University Institute of Science and Technology, Istanbul, Turkey.
• Yılmaz, S., Çakır, B., Gedik, A. & Dinçer, H. (1999). Speed control of a conveyor system by means of fuzzy control aided PLC. ISIE '99 - Proceedings of the IEEE Int. Symposium on Industrial Electronics, Bled, Slovenia, vol. 3, 1328-1332. DOI: https://doi.org/10.1109/ISIE.1999.796896
• Young, H. L. & Kopp, R. (2011). Application of fuzzy control for a hydraulic forging machine. Fuzzy Sets and Systems, 118(1), 99-108. DOI: https://doi.org/10.1016/S0165-0114(98)00464-3