Based on the Application of Self-Learning Fuzzy PID Control in Tension Control System

Gang Yuan Mao; Mei Wu; Hui Kang Liu

doi:10.4028/www.scientific.net/AMR.655-657.1505

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 655-657Based on the Application of Self-Learning Fuzzy...

Based on the Application of Self-Learning Fuzzy PID Control in Tension Control System

Abstract:

Tension control system is one of the widely used systems in modern industrial control systems. In tension control system PID parameter setting is one of the main problems affecting the control system performance. However, conventional PID parameter is difficult to set. This article describes a PID fuzzy controller based on adaptive learning, which can change according to the deviation and deviation rate to adjust parameters in real time. Practice shows that such self-learning fuzzy PID controller has better control performance than conventional PID controller in tension control.

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Periodical:

Advanced Materials Research (Volumes 655-657)

Pages:

1505-1509

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.655-657.1505

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Online since:

January 2013

Authors:

Gang Yuan Mao, Mei Wu, Hui Kang Liu

Keywords:

Fuzzy Control, PID Parameter Tuning, Self-Learn, Tension Control

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References

[1] Juanjuan Wang, Chuang Fu, and Yao Zhang. SVC Control System Based on Instantaneous Reactive Power Theory and Fuzzy PID [J]. Industrial Electronics, IEEE Transactions on, 2008, 55(4): 1658–1665.

DOI: 10.1109/tie.2007.911933

Google Scholar

[2] Rong-Jong Wai, Meng-An Kuo, and Jeng -Dao Lee. Design of Cascade Adaptive Fuzzy Sliding-Mode Control for Nonlinear Two-Axis Inverted -Pendulum Servomechanism [J]. Fuzzy Systems, IEEE Transactionson, 2008, 16(5): 1232-1244.

DOI: 10.1109/tfuzz.2008.924277

Google Scholar

[3] Han-Xiong Li, Shaocheng Tong. A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems [J]. Fuzzy Systems, IEEE Transactions on, 2003, 11(1): 24-34.

DOI: 10.1109/tfuzz.2002.806314

Google Scholar

[4] Acampora G, Loia V. Fuzzy control interoperability and scalability for adaptive domotic framework [J]. IEEE Transactions on Industrial Informatics, 2005, 11(1): 97-111.

DOI: 10.1109/tii.2005.844431

Google Scholar

[5] Youshan Li. fuzzy control theory and its application in the process control [m]. in Beijing, national defense industry press, (1993).

Google Scholar

[6] Haifang Liu, Gillian Zhao. application of fuzzy PID control in the control system of shuttle kiln [j]. Shandong ceramics, 2008, (02).

Google Scholar

[7] Yonghua Tao. new PID control and its application [m]. Beijing: China machine press, (1998).

Google Scholar

[8] Zixing Cai. basis and application of intelligence control-[m]. Beijing: national defense industry press, (1998).

Google Scholar