Research on the Application of Intelligent Decoupling Control Method in the Process Control System

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Abstract:

There is a universal coupling in process control system, especially with the development of science and technology, industry system has became more and more big, the control process is more complicated, the variables which need to solve are also gradually increased. Therefore between the variables in the associated process produce coupling. In order to solve the coupling problems, we introduced PI self-adjusting fuzzy control model in the link of decoupling, and designed the model and the algorithm of decoupling with decoupling controller. The experiment showed that, after the introduction of PI algorithm, the time when the system reaches a steady is greatly reduced, saving time and cost. Also there is no coupling between the two signals, which shows that PI algorithm successfully reaches the decoupling in the process control.

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Periodical:

Advanced Materials Research (Volumes 846-847)

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339-342

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November 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Zhixiang Deng, Chuanfeng Li, Yongji Wang. Robust Decoupling Control of BTT Gliding Vehicle [J]. Aerospace Control, 2011, 27 (3): 56-64.

DOI: 10.1109/icicisys.2009.5358347

Google Scholar

[2] Jun Zhang, Dean Zhao, Mei Wang. Robust Decoupling Control Method of A Hypersonic Vehicle [J]. Journal of Astronautics, 2011, 32 (5): 1100-1107.

Google Scholar

[3] Min Wu, Guoxiong Zhou, Weihua Cao. Fuzzy Decoupling Control of the Asymmetrical Gas Collector Pressure [J]. Theory and Applications of Control, 2010, 27 (1): 94-98.

Google Scholar

[4] Guoyong Li. Theory and Application of Fuzzy Neural Control [M]. Beijing Electronic Industry Press, 2012: 2-3.

Google Scholar

[5] Haipeng Pan, Yuying Xu. PID Decoupling Control based on the BP network of Headbox with Double Variable [J]. Journal of Chemical and Engineering, 2010, 61 (8): 2154-2158.

DOI: 10.1109/wcica.2010.5554526

Google Scholar

[6] Shihong Xie. Example of Control Dynamic Simulation for MATLAB R2008 [M]. Beijing: Chemical Industry Press, 2012: 145-147.

Google Scholar

[7] Suzuki R, Torita T, Kobayashi N, Hofer E P. Internal model control scheme for sensorless force control and its application to rubbing machines[C]. Singapore: IEEE Conference on Industrial Electronic and Applications, 2010: 1-6.

DOI: 10.1109/iciea.2006.257156

Google Scholar

[8] El-Sousy F F M. An intelligent model-following sliding-mode position controller for PMSM servo drives[C]. In Proceedings of the 2007 4th IEEE International Conference on Mechatronics. Kumamoto, Japan: IEEE, Press. 2011: 1-6.

DOI: 10.1109/icmech.2007.4280070

Google Scholar

[9] Bo Zhang, Genbao Zhang, Yan Li. Fuzzy Adaptive Decoupling Predictive Control of Quantitative Water [J]. Journal of China Pulp, 2011, 22 (3): 94-96.

Google Scholar

[10] Jin Yan. Algorithm of Decoupling and Coordination Control for Gas Gathering Process of Coke Oven[D]. Central South University, 2010 (2): 26-56.

Google Scholar

[11] Zhigang Xiao. The design of coke oven collector pressure control based on theory of fuzzy [J]. Science Technology and Engineering 2010, (12): 2983-2987.

Google Scholar

[12] Mingyong Wu, Guowei Wang. Intelligent control of boiler drum water level based on OPC and MATLAB [J]. Computer Measurement and Control, 2010, 18 (10): 2296-2298.

Google Scholar

[13] LI Z.Support vector machine model based predictive pid control system for cement rotary kiln [C]. Control and Decision Con-frence, 2010: 3117-3121.

DOI: 10.1109/ccdc.2010.5498646

Google Scholar