Abstract
In unit steam-boiler generation, a coordinated control strategy is required to ensure a higher rate of load change without violating thermal constraints. The process is characterized by nonlinearity and uncertainty. Using of neuro-fuzzy networks (NFNs) to represent a nonlinear dynamical process is one choice. Two alternative methods of exploiting the NFNs within a generalised predictive control (GPC) framework are described. Coordinated control of steam-boiler generation using the two nonlinear GPC methods show excellent tracking and disturbance rejection results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Katebi, M.R., Johnson, M.A.: Predictive Control Design for Large-scale Systems. Automatica 33(3), 421–425 (1997)
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)
Brown, M., Harris, C.J.: Neurofuzzy Adaptive Modelling and Control. Prentice-Hall, Englewood Cliffs (1994)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Liu, XJ., Liu, JZ. (2007). Neurofuzzy Power Plant Predictive Control. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-540-72393-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
eBook Packages: Computer ScienceComputer Science (R0)