Abstract
The deaerator pressure and deaerator water level are intercoupling in marine steam power plant. Traditional PID control strategy is difficult to get satisfactory control effect. We must take corresponding decoupling measures. This paper proposes a deaerator pressure and deaerator water level decoupling control strategy based on PID neural network, with which we can make comprehensive utilization of the advantage of both PID and neural network. Results of the simulation show that compared with traditional PID control strategy, the PID neural network decoupling control strategy can provide more stability and faster response speed in deaerator pressure and deaerator water level control.
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.
References
Mahumod, F., Tarek, A.: Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network. Intelligent Control and Automation 02(03), 176–181 (2011)
Li, H.J., Chen, M.J.: Design of decoupling PID controller for a kind of practical engineering. Control Engineering of China 15(3), 275–278 (2008) (in Chinese)
Wu, J., Xu, Z.B., Ma, X.Q.: Numerical simulation on water level control model for deaerators in nuclear power plants. Thermal Power Generation (3), 47–51 (2014)
Yin, W.: Research on Dynamic Modeling and Control method of Marine Condensation-steam System. Harbin Engineering University, Harbin (2008) (in Chinese)
Sun, X.J., Shi, J., Yang, Y.: Neural Networks Based Attitude Decoupling Control for AUV with X-Shaped Fins. Advanced Materials Research 2717(819), 222–228 (2013)
Shu, H., Shu, H.L.: Simulation of PID Neural Network Control System with Virtual Instrument. In: Proceedings of Asia Simulation Conference 2008/the 7th International Conference on System Simulation and Scientific Computing (ICSC 2008), p. 4 (2008)
Shu, H.L., Hu, J.T.: Study on Multivariable System Based on PID Neural Network Control. Advanced Materials Research 2076(591), 1490–1495 (2012)
Cheng, Q.M., Zheng, Y.: Multi-variable PID neural network control systems and their application to coordination control. East China Electric Power 11, 54–58 (2007) (in Chinese)
Sun, S.Q., Li, S.: Application of PID Neural Network in Head box Multivariable Decoupling Control. In: 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 2427–2430. IEEE (2012)
Shu, H.L.: Analysis of PID neural network multivariable control systems. Acta Automatica Sinica 25(1), 105–111 (1999) (in Chinese)
Guo, A.W., Yang, J.D., Bao, H.Y.: PID Neural Network Decoupling Control for Doubly Fed Hydro-generator System. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pp. 6149–6152. IEEE (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, P., Meng, H., Ji, Qz. (2014). Application of PID Neural Network Decoupling Control in Deaerator Pressure and Deaerator Water Level Control System. In: Tanaka, S., Hasegawa, K., Xu, R., Sakamoto, N., Turner, S.J. (eds) AsiaSim 2014. AsiaSim 2014. Communications in Computer and Information Science, vol 474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45289-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-45289-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45288-2
Online ISBN: 978-3-662-45289-9
eBook Packages: Computer ScienceComputer Science (R0)