Control of the temperature and humidity regime of the seed cotton drying process based on a fuzzy PID controller
Keywords:
raw cotton, drying, moisture content, fuzzy logic, PID controller, adaptive control, temperatureAbstract
An adaptive control structure for the temperature-moisture regime of raw cotton drying based on a fuzzy-PID controller is considered. The drying process is treated as a multivariable nonlinear inertial plant affected by changes in initial moisture, cotton flow rate, drying-agent temperature and flow rate. A fuzzy supervisor is proposed to update Kp, Ki and Kd online using the control error and its rate of change. Linguistic variables, membership functions and principles for a 49-rule Mamdani inference base are formulated. The proposed structure provides a theoretical basis for adapting the controller to varying drying conditions and stabilizing the outlet moisture content around its set point.
References
Byler R.K., Anthony W.S. Initial Experiences in Computer Control of Cotton Gin Drying // Applied Engineering in Agriculture. 1992. Vol. 8, No. 5. P. 703–709. DOI: 10.13031/2013.26142.
Baker K.D. Temperature Control for Seed Cotton Drying Systems // Applied Engineering in Agriculture. 2012. Vol. 28, No. 6. P. 885–890.
Yunusnova S.T., Holmatov D.A., Atajonov M.O., Huzanazarov U. Formalization of the Cotton Drying Process Based on Heat and Mass Transfer Equations // IIUM Engineering Journal. 2020. Vol. 21, No. 2. P. 256–265. DOI: 10.31436/iiumej.v21i2.1456.
Zadeh L.A. The Concept of a Linguistic Variable and its Application to Approximate Reasoning // Information Sciences. 1975. Vol. 8. P. 199–249.
Mamdani E.H. Application of Fuzzy Algorithms for Control of Simple Dynamic Plant // Proceedings of the Institution of Electrical Engineers. 1974. Vol. 121, No. 12. P. 1585–1588.
Hosseinpour S., Martynenko A. Application of Fuzzy Logic in Drying: A Review // Drying Technology. 2022. Vol. 40, No. 5. P. 797–826. DOI: 10.1080/07373937.2020.1846192.
Feng X., Guo C., Li P., Yang B., Li H., Gao H., Zhang C. A Study on Drying Control of Seed Cotton Based on BP Neural Network Model // IOP Conference Series: Earth and Environmental Science. 2021. Vol. 697. 012007. DOI: 10.1088/1755-1315/697/1/012007.
Yunusova S.T. Simulation of a Trained Neural Network of a Fuzzy Logic Regulation System Based on the Cotton Drying Process // Technical Science and Innovation. 2020. ROI: 11.0008/A0120-0030.
Siddikov I.X., Yunusova S.T., Xujanazarov U.O. Nechetko-logicheskoye regulirovaniye protsessa sushki xlopka // Muhammad Al-Xorazmiy avlodlari. 2020. No. 2(12). P. 43–47.
Hosseinpour S., Martynenko A. An Adaptive Fuzzy Logic Controller for Intelligent Drying // Drying Technology. 2023. Vol. 41, No. 7. P. 1110–1132. DOI: 10.1080/07373937.2022.2119996.
Усков А.А., Кузьмин А.В. Интеллектуальные технологии управления. Искусственные нейронные сети и нечеткая логика. – М.: Горячая Линия – Телеком, 2004. – 143 с.
Yusupbekov N.R., Aliyev R.A., Aliyev R.R., Yusupbekov A.N. Boshqarishning intellektual tizimlari va qaror qabul qilish. Toshkent: O‘zbekiston milliy ensiklopediyasi, 2015. 490 b.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
