A Nonlinear Model Predictive Control Strategy Using Multiple Neural Network Models

  • Zainal Ahmad
  • Jie Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Combining multiple neural networks appears to be a very promising approach for improving neural network generalization since it is very difficult, if not impossible, to develop a perfect single neural network. Therefore in this paper, a nonlinear model predictive control (NMPC) strategy using multiple neural networks is proposed. Instead of using a single neural network as a model, multiple neural networks are developed and combined to model the nonlinear process and then used in NMPC. The proposed technique is applied to water level control in a conic water tank. Application results demonstrate that the proposed technique can significantly improve both setpoint tracking and disturbance rejection performance.


Manipulate Variable Nonlinear Model Predictive Control Tank Level Single Neural Network Multiple Neural Network 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zainal Ahmad
    • 1
  • Jie Zhang
    • 2
  1. 1.School of Chemical EngineeringUniversity Sains MalaysiaNibong Tebal, PenangMalaysia
  2. 2.School of Chemical Engineering and Advanced MaterialsUniversity of NewcastleNewcastle upon TyneUK

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