Abstract
Overcoming the coupling impact is the premise to achieve rapid, precise and especially independent control for the two concatenate pressures of the double-level air current field (DACF) system. Due to the nonlinearity, time lag, and strong coupling characteristics of the system, a decoupling method based on neural networks and prediction principle is presented in this paper. With the neural networks, a nonlinear mathematical model of the relationship describing air flow rate and other variations including the upstream pressure, the downstream pressure and valve opening is developed. With the prediction principle, the predicted pressure state formula is derived. On the basis of them, the predictive expressions of disturbances between the upstream and downstream pressure are obtained by the ideal gas equation. Thereby the controller outputs are regulated on line properly in advance, and the coupling disturbances and time lag effect are weakened notably. Experimental results show the method is effective to achieve the system decoupling.
This work is partially supported by a special key project of National Public Sector of China (Grant No. GYHY2007060003).
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References
Ma, P., Fang, J., Cui, C., Hu, S.: Current situation and development of decoupling Control. Control Engineering of China 12(2), 97–100 (2005)
Sang, B., Xue, X.: A Summary of multivariable decoupling methods. Fire Control and Command Control 32(11), 13–16 (2007)
Lee, T., Nie, J., Lee, M., et al.: A fuzzy controller with decoupling for multivariable nonlinear servo mechanisms, with application to real-time control of a passive line-of-sight stabilization system. Mechatronics 7(1), 83–104 (1997)
Zheng, Q., Chen, Z., Gao, Z.: A practical approach to disturbance decoupling control. Control Engineering Practice 17(9), 1016–1025 (2009)
Jin, Q., Zeng, D., Wang, Y., Gu, S.: New Decoupling Method Based on Neural Network for Multivariable System. Journal of Northeastern University (Natural Science) 20(3), 250–253 (1999)
Li, H.: Design of multivariable fuzzy-neural network decoupling controller. Control and Decision 21(5), 593–596 (2006)
Chai, T., Mao, K., Qin, X.: Decoupling design of multivariable generalized predictive control. IEE Proceedings-Control Theory and Application 141(3), 197–201 (1994)
Su, B., Chen, Z., Yuan, Z.: Multivariable Decoupling Predictive Control with Input Constraints and Its Application on Chemical Process. Chinese Journal of Chemical Engineering 14(2), 216–222 (2006)
Li, J., Yang, M., Jiang, P.: Multivariable dynamic matrix decoupling control for strong coupling temperature object. Control Engineering of China 13(5), 423–425 (2006)
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© 2011 Springer-Verlag Berlin Heidelberg
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Dong, D., Meng, X., Liang, F. (2011). Decoupling Control for DACF Pressure Based on Neural Networks and Prediction Principle. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23235-0_76
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DOI: https://doi.org/10.1007/978-3-642-23235-0_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23234-3
Online ISBN: 978-3-642-23235-0
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