Sliding Mode Control of a Wastewater Treatment Plant with Neural Networks

  • Miguel A. Jaramillo-Morán
  • Juan C. Peguero-Chamizo
  • Enrique Martínez de Salazar
  • Montserrat García del Valle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)


In this work a sliding mode control carried out by neural networks and applied to a wastewater treatment plant is proposed. The controller has two modules: the first one performs the plant control when its dynamics lies inside an optimal working region and is carried out by a neural network trained to reproduce the behavior of the technician who controls an actual plant, while the second one drives the system dynamics towards that region when it works outside it and is carried out by another neural network trained to perform that task. Both controllers are combined with a two layers neural network where the synaptic weights of the only neuron in the second one is adjusted by those in the previous layer in order to balance the contribution of each controller to the total control action.


Neural Network Wastewater Treatment Plant Synaptic Weight Aeration Tank Cellular Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    DeCarlo, R.A., Zak, S.H., Drakunov, S.V.: Variable Structure, Sliding-Mode Controller design. In: Levine, S.W. (ed.) The Control Handbook, pp. 941–951. CRC Press-IEEE Press (1996)Google Scholar
  2. 2.
    Tsai, C.H., Chung, H.Y., Yu, F.M.: Neuro-Sliding Mode Control with its Applications to Seesaw Systems. Trans. on Neural Networks 15(1), 124–134 (2004)CrossRefGoogle Scholar
  3. 3.
    Wang, J., Rang, A.B., Chang, P.T.: Indirect Adaptive Fuzzy Sliding Mode Control: Part I: Fuzzy Switching. Fuzzy Sets and Systems 122, 21–30 (2001)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    E.D.A.R. Aguas de Mérida S. L.: Exploitation and maintenance report (1998 and 1999) Google Scholar
  5. 5.
    Ramalho, R.S.: Introduction to Wastewater Treatment Processes. Academic Press, London (1983)Google Scholar
  6. 6.
    Olsson, G., Newell, B.: Wastewater Treatment System. IWA (1999)Google Scholar
  7. 7.
    Norgaard, M.: Neural Networks for Modeling and Control of Dynamic Systems. Springer, Heidelberg (2000)Google Scholar
  8. 8.
    Chua, L.O., Yang, L.: Cellular Neural Networks: Theory. IEEE Trans. on Circuits and Systems 35(10), 1257–1272 (1988)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Jaramillo-Morán, M.A., Fernández-Muñoz, J.A.: Adaptive Adjustment of the CNN Output Function to Obtain Contrast Enhancement. In: Mira, J. (ed.) IWANN 1999. LNCS, vol. 1607, pp. 412–421. Springer, Heidelberg (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Miguel A. Jaramillo-Morán
    • 1
  • Juan C. Peguero-Chamizo
    • 2
  • Enrique Martínez de Salazar
    • 1
  • Montserrat García del Valle
    • 1
  1. 1.E. de Ingenierías IndustrialesUniversity of ExtremaduraBadajozSpain
  2. 2.Centro Universitario de MéridaMéridaSpain

Personalised recommendations