Abstract
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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Liu, X. J., & Liu, J. Z. (2006). Neuro-fuzzy generalized predictive control of boiler steam temperature. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3972 LNCS, pp. 1027–1032). Springer Verlag. https://doi.org/10.1007/11760023_151
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