The Application of GPC Decoupling Control in Tubular Furnace


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Furnace as a control system whose conditions complex, parameter changing , great runing inertia ,control action delay . Of which there are many uncertainties ,such as air, fuel gas pressure and the frequent fluctuations of the fuel’s calorific value . The impact of mutual interference and coupling between the variables to the furnace can not be ignored , though it’s not dominant .The systerm based on control model use an advanced generalized predictive control (GPC) algorithm control mode .It can improve the control accuracy of regulator action when the furnace is at the condition of stability .The experiment results show that ducoupling GPC algorithm has a good convergence effect .



Key Engineering Materials (Volumes 480-481)

Edited by:

Yanwen Wu




Z. P. Chen and X. J. Li, "The Application of GPC Decoupling Control in Tubular Furnace", Key Engineering Materials, Vols. 480-481, pp. 1432-1437, 2011

Online since:

June 2011




[1] Gao Jianping, Rohit. Performance evaluation of two industrial MPC controllers. Control Engineering Practice, Vol. 11(2003), pp.1371-1387.

DOI: 10.1016/s0967-0661(03)00115-1

[2] Rodrigues J A D, Toledo E C V. A tuned approach of the predictive adaptive GPC controller applied to a fed batch bioreactor using complete factorial design. Com2puters and Chemical Engineering, Vol. 26(2003), pp.1493-1500.

DOI: 10.1016/s0098-1354(02)00099-6

[3] Wu Xiao-fan. An Identification Method on Multivariate System Model of Heating Furnace, Automation in Petro-Chemical Industry, Vol . 1(2003), pp.23-26.

[4] Wei Guo, Liu Jian, Lei Miao, et al. Modeling approach to nonlinear MISO sensor system based on B-spline recursive least-squares, Chinese Journal of Scientific Instrument, Vol. 7(2009), pp.1404-1409.

[5] Li Yong-gang, He Yan-ping, Yang Yu. Application of ARMA Model and Identification of Model's Order to Simulation of Wind[J], East China Electric Power, Vol3. ( 2010), pp.395-398.

[6] L. Chi-Huang, T. Ching-Chih, Generalized predictive control using recurrent fuzzy neural networks for industrial processes, Journal of Process Control, Vol. 17 (2007), pp.83-92.

DOI: 10.1016/j.jprocont.2006.08.003

[7] L. Qiang, Q. Baocun, G. Zhiqiang, Z. Xisheng, Study of Fuzzy Generalized Predictive Control Algorithm on Nonlinear Systems, International Conference on Innovative Computing, Information andControl, (2006), pp.437-440.

DOI: 10.1109/icicic.2006.159

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