Neuro-fuzzy Generalized Predictive Control of Boiler Steam Temperature

  • Xiang-Jie Liu
  • Ji-Zhen Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper, which consists of local GPCs designed using the local linear models of the neuro-fuzzy network. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant, in which much better performance than the traditional cascade PI controller or the linear GPC is obtained.


Steam Temperature Generalize Predictive Controller Superheated Steam Temperature 200MW Power Plant Neurofuzzy Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, X.J., Lara-Rosano, F., Chan, C.W.: Neurofuzzy Network Modelling and Control of Steam Pressure in 300MW Steam-Boiler System. Engineering Applications of Artificial Intelligence 16(5), 431–440 (2003)CrossRefGoogle Scholar
  2. 2.
    Silva, R.N., Shirley, P.O., Lemos, J.M., Goncalves, A.C.: Adaptive Regulation of Super-heated Steam Temperature: A Case Study in an Industrial Boiler. Control Engineering Practice 8(8), 1405–1415 (2000)CrossRefGoogle Scholar
  3. 3.
    Moelbak, T.: Advanced Control of Superheater Steam Temperatures - An Evaluation Based on Practical Applications. Control Engineering Practice 7(7), 1–10 (1999)CrossRefGoogle Scholar
  4. 4.
    Prasad, G., Swidenbank, E., Hogg, B.W.: A Neural Net Model-based Multivariable Long-range Predictive Control Strategy Applied Thermal Power Plant Control. IEEE Trans. Energy Conversion 13(2), 176–182 (1998)CrossRefGoogle Scholar
  5. 5.
    Brown, M., Harris, C.J.: Neurofuzzy Adaptive Modelling and Control. Prentice-Hall, Englewood Cliffs (1994)Google Scholar
  6. 6.
    Liu, X.J., Lara-Rosano, F., Chan, C.W.: Model-Reference Adaptive Control Based on Neurofuzzy Networks. IEEE Trans. Systems, Man and Cybernetics C 34(3), 302–309 (2004)CrossRefGoogle Scholar
  7. 7.
    Clarke, D.W., Mohtadi, C., Tuffs, P.S.: Generalized Predictive Control, Parts 1 and 2. Automatica 23(2), 137–160 (1987)MATHCrossRefGoogle Scholar
  8. 8.
    Liu, X.J., Zhou, X.X.: Identification of Boiler Models and its Fuzzy Logic Strategy. In: Proc. the 14th IFAC World Congress, Beijing China, June, vol. O, pp. 149–154 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiang-Jie Liu
    • 1
  • Ji-Zhen Liu
    • 1
  1. 1.Department of AutomationNorth China Electric Power UniversityBeijingP.R. China

Personalised recommendations