Memristive Radial Basis Function Neural Network for Parameters Adjustment of PID Controller

  • Xiaojuan Li
  • Shukai DuanEmail author
  • Lidan Wang
  • Tingwen Huang
  • Yiran Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)


Radial basis function (RBF) based-identification proportional–integral–derivative (PID) can automatically adjust the parameters of PID controller with strong self-organization, self-learning and self-adaptive ability. However, the compound controller has complex weight updating algorithm and large calculation. Memristor, applied well to the investigation of storage circuit and artificial intelligence, is a nonlinear element with memory function. Thus, it can be introduced to RBF neural network as electronic synapse to save and update the synaptic weights. This paper builds a model of memristive RBF-PID (MRBF-PID), and proposes the updating algorithm of weight upon memristance. The proposed MRBF-PID is used for the control of a nonlinear system. Its controlling effect is showed by numerical simulation experiment.


Memristor Radial basis function neural network PID controller Simulink model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chua, L.O.: Memristor-The Missing Circuit Element. IEEE Transactions on Circuit Theory 18(5), 507–519 (1971)CrossRefGoogle Scholar
  2. 2.
    Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The Missing Memristor Found. Nature 453(7191), 80–83 (2008)CrossRefGoogle Scholar
  3. 3.
    Williams, R.S.: How We Found the Missing Memristor. IEEE Spectrum 45(12), 28–35 (2008)CrossRefGoogle Scholar
  4. 4.
    Duan, S., Zhang, Y., Hu, X., et al.: Memristor-based Chaotic Neural Networks for Associative Memory. Neural Computing and Applications, 1–9 (2014)Google Scholar
  5. 5.
    Zhang, Y., Liu, C., Song, X., et al.: Application of RBF Neural Network Controller in the Rectification Column Temperature Control System. In: International Symposium on Computational Intelligence and Design, pp. 72–75 (2013)Google Scholar
  6. 6.
    Wang, L., Fang, X., Duan, S., et al.: PID Controller Based on Memristve CMAC Network. Abstract and Applied Analysis 2013, 1–6 (2013)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Xue, Y., Ye, J., Qian, H., et al.: The Research of Complex BP Neural Network PID Control. In: International Conference on Artificial Intelligence and Computational Intelligence, pp. 55–58. IEEE Press, New York (2009)Google Scholar
  8. 8.
    Zhou, Y., Ding, Q.: Study of PID Temperature Control for Reactor Based on RBF Network. In: 2012 International Conference on Automation and Logistics, pp. 456–460. IEEE Press, New York (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaojuan Li
    • 1
  • Shukai Duan
    • 1
    Email author
  • Lidan Wang
    • 1
  • Tingwen Huang
    • 2
  • Yiran Chen
    • 3
  1. 1.College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.Department of Electrical and Computer EngineeringTexas A&M UniversityDohaQatar
  3. 3.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA

Personalised recommendations