Abstract
Based on the analysis of its operational mechanism, an improved nonlinear model is developed for a medium-speed pulverizer. This is achieved by identifying a group of constant coefficients for a set of nonlinear differential equations with the aid of an improved genetic algorithm. The main objective of this research is to convert the nonlinear model into a T-S fuzzy model composed of several linear models, enabling easy design of the control system for the pulverizer. The simulation results show a satisfactory agreement between the T-S fuzzy model response and the measured data, confirming the effectiveness of the proposed method. Moreover, the proposed modeling method can be easily applied to other nonlinear systems, given that their nonlinear differential equations are known “a priori”.
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References
Zhou, G., Si, J., Taft, C.W.: Modeling and simulation of C-E deep bowl pulverizer. IEEE Transactions on Energy Conversion 3, 312–322 (2000)
Zhang, Y.G., Wu, Q.H., Wang, J., Matts, D., Zhou, X.X.: Pulverizer modeling by machine learning based on onsite measurements. IEEE Transactions on Energy Conversion 4, 549–555 (2002)
Tanaka, S., Kurosaki, Y., Teramoto, T., Murakami, S.: Dynamic simulation analysis of MPS mill for coal fired boiler and application of its results to boiler control system. In: IFAC Symposium of Power Plant and Control, Beijing, China (1997)
Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on System, Man, and Cybernetics 1, 116–132 (1985)
Wu, H.N., Zhang, H.Y.: Reliable mixed L-2/H-infinity fuzzy static output feedback control for nonlinear systems with sensor faults. Automatica 11, 1925–1932 (2005)
Lin, C., Wang, Q.G., Lee, T.H.: Improvement on observer-based H-infinity control for T-S fuzzy systems. Automatica 9, 1651–1656 (2005)
Chen, S.S., Chang, Y.C., Su, S.F.: Robust static output feedback stabilization for nonlinear discrete-time systems with time delay via fuzzy control approach. IEEE Transactions on Fuzzy Systems 2, 263–272 (2005)
Liu, H.P., Sun, F.C., Sun, Z.Q.: Stability analysis and synthesis of fuzzy singularly perturbed systems. IEEE Transactions on Fuzzy Systems 2, 273–284 (2005)
Li, K., Thompson, S., Peng, J.: Modelling and prediction of NOx emission in a coal-fired power generation plant. Control Engineering Practice 12, 707–723 (2004)
Huang, X.Y.: Operation and combustion regulation of boiler in power plants. Chinese electric power press, Beijing (2003)
Leung, F.H.F., Lam, H.K., Ling, S.H., Tam, P.K.S.: Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Transactions on Neural Networks 1, 79–88 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, J., Fei, M., Li, K., Zhu, Q. (2006). Fuzzy Modeling of a Medium-Speed Pulverizer Using Improved Genetic Algorithms. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_159
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DOI: https://doi.org/10.1007/11816157_159
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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