KaydedilenlerDerginizi ekleyin

Dergiler / International Journal of Intelligent Systems and Applications in Engineering / 2018 / Cilt: 6 - Sayı: 2

Fuzzy and Taguchi based Fuzzy Optimization of Performance Criteria of the Process Control Systems

Fatih KARA
VEDAT ARDA KÜÇÜK
Barış ŞİMŞEK
Sayfa
138–143
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In comparison with the classical PID method, in the Fuzzy PID method, the improvement is calculated as 117% in the average of the offset value and 14670% in the standard deviation.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:This paper proposes a Taguchi based Fuzzy and Fuzzy PID application using MATLAB® version 2015a to assess and optimize of process control performance criteria of liquid level and flow rate control system.
📊
Temel Bulgular & Veri:When the main effect graphs for the liquid level and flow rate control system are evaluated, it was seen that the change in the membership function is the most effective factor on the process control performance.
💡
Sonuç & Katkı:In comparison with the classical PID method, in the Fuzzy PID method, the improvement is calculated as 117% in the average of the offset value and 14670% in the standard deviation.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

This paper proposes a Taguchi based Fuzzy and Fuzzy PID application using MATLAB® version 2015a to assess and optimize of process control performance criteria of liquid level and flow rate control system. When the main effect graphs for the liquid level and flow rate control system are evaluated, it was seen that the change in the membership function is the most effective factor on the process control performance. It can be said that the Gaussian membership function provides the lowest mean and standard deviation in the offset value. Improvement rates for “overshoot”, “rise time”, “first peak time”, “%95 setting time, “%99 setting time”, “mean” and “the standard deviation of the offset values” are %102, %99, %120, %344, %181, %5, %167 for flow rate control system; %102, %97, %124, %77, %91, %4, %167 for liquid level control system in order. In comparison with the classical PID method, in the Fuzzy PID method, the improvement is calculated as 117% in the average of the offset value and 14670% in the standard deviation.

🔖

Bu Makaleye Atıf Yap

KARA, F., KÜÇÜK, V. A., ŞİMŞEK, B. (2018). Fuzzy and Taguchi based Fuzzy Optimization of Performance Criteria of the Process Control Systems. International Journal of Intelligent Systems and Applications in Engineering, Cilt: 6 - Sayı: 2, 138–143. https://ajindex.com/articles/167203

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (13)

  1. Zhang, R., J. Tao, and F. Gao, A New Approach of Takagi–Sugeno Fuzzy Modeling Using an Improved Genetic Algorithm Optimization for Oxygen Content in a Coke Furnace. Industrial & Engineering Chemistry Research, 2016. 55(22): p. 6465-6474
  2. Padula, F., Ionescu, C., Latronico, N., Paltenghi, M., Visioli, A., and Vivacqua, G. Optimized PID control of depth of hypnosis in anesthesia. Computer Methods and Programs in Biomedicine, 2017. 144(Supplement C): p. 21-35
  3. Taysom, B.S., C.D. Sorensen, and J.D. Hedengren, A comparison of model predictive control and PID temperature control in friction stir welding. Journal of Manufacturing Processes, 2017. 29(Supplement C): p. 232-241
  4. Trafczynski, M., Markowski, M., Alabrudzinski, S., and Urbaniec, K. (2016). The influence of fouling on the dynamic behavior of PID- controlled heat exchangers. Applied Thermal Engineering, 2016. 109(Part A): p. 727-738
  5. Beschi, M., F. Padula, and A. Visioli, Fractional robust PID control of a solar furnace. Control Engineering Practice, 2016. 56(Supplement C): p. 190-199
  6. Thenozhi, S. and W. Yu, Stability analysis of active vibration control of building structures using PD/PID control. Engineering Structures, 2014. 81(Supplement C): p. 208-218
  7. Zhang, J., Design of a new PID controller using predictive functional control optimization for chamber pressure in a coke furnace. ISA Transactions, 2017. 67(Supplement C): p. 208-214
  8. Zhang, R., Wu, S., Lu, R., and Gao, F., Predictive control optimization based PID control for temperature in an industrial surfactant reactor. Chemometrics and Intelligent Laboratory Systems, 2014. 135(Supplement C): p. 48-62
  9. Zarei, M., Ghaderi, R., Kojuri, N., and Minuchehr, A., Robust PID control of power in lead cooled fast reactors: A direct synthesis framework. Annals of Nuclear Energy, 2017. 102(Supplement C): p. 200-209
  10. Lamba, R., S.K. Singla, and S. Sondhi, Fractional order PID controller for power control in perturbed pressurized heavy water reactor. Nuclear Engineering and Design, 2017. 323(Supplement C): p. 84-94
  11. Wang, Y., Q. Jin, and R. Zhang, Improved fuzzy PID controller design using predictive functional control structure. ISA Transactions, 2017. 71(Part 2): p. 354-363
  12. Rakhtala, S.M. and E. Shafiee Roudbari, Fuzzy PID control of a stand- alone system based on PEM fuel cell. International Journal of Electrical Power & Energy Systems, 2016. 78(Supplement C): p. 576- 590
  13. Liu, F. and H. Wang, Fuzzy