Research of the Pipe Flow Measurement and Control System Based on BP Neural Networks PID

  • Kai-ming HuEmail author
  • Yue-zhong Li
  • Xiao-ming Guan
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 165)


As the modern industrial technology development, the accuracy of flow control has become increasingly demanding. The pipeline flow measurement and control system comes under the infulences such as its own attributes, the liquid friction agencies, the impact of noise. The control plant has a certain lag time delay and capacity delay characteristics. In this paper a pipe flow measurement and control system based on neural networks PID algorithm is expounded. It establishes the system’s mathematical model, designs a suitable BP neural network PID controller for the system, and carries on the simulation. This design can well solve the control request of the flow system that can not meet the precise need because of the time-varying complicated nonlinearity characteristics.


BP neural network PID flow system’s model simulation 


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  1. 1.
    Fung, Q.-S., et al.: A flow current control system based on step digital closed-loop control. Manufacturing Technology and Machine Tool 6, 93–95 (2008)Google Scholar
  2. 2.
    Di, H., et al.: A New Phase Acquisition Method of Address Code Utilizing the Combination of Logistic Map and BP Network. Circuits, Systems, and Signal Processing 22(1), 1–17 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Wang, Z.-L., Guo, Y.-K.: Process control and Simulink applications. Electronic Industry Press, Beijing (2006)Google Scholar
  4. 4.
    Wang, Z.-Y., et al.: Temperature-Variation Fault Diagnosis of the High-Voltage Electric Equipment Based on the BP Neural Network. In: Advances in Neural Networks, pp. 633–640 (2007)Google Scholar
  5. 5.
    Liu, J.: Advanced PID Control and MATLAB Simulation. Electronic Industry, BeijingGoogle Scholar
  6. 6.
    Kim, J.H., Oh, S.J.: A fuzzy PID controller for nonlinear and uncertain systems. Soft Computing 4, 123–129 (2000)CrossRefGoogle Scholar
  7. 7.
    Oh, S.-K., Roh, S.-B.: The Design of Fuzzy Controller by Means of Evolutionary Computing and Neurofuzzy Networks. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 1080–1083. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of Information and Electronic EngineeringEast China Institute of TechnologyFuzhouChina

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