Adaptive Pseudo Linear RBF Model for Process Control

  • Ding-Wen Yu
  • Ding-Li Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


A pseudo-linear radial basis function (PLRBF) network is developed in this paper. This network is used to model a real process and its weights are on-line updated using a recursive orthogonal least squares (ROLS) algorithm. The developed adaptive model is then used in model predictive control strategy, which is applied to a pilot multivariable chemical reactor. The first stage of the project, simulation study, has been investigated and is presented. The effectiveness of the adaptive control in improving the closed-loop performance has been demonstrated for process time-varying dynamics and model-process mismatch.


Model Predictive Control Radial Basis Function Neural Network Adaptive Model Internal Model Control Hide Layer Node 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ding-Wen Yu
    • 1
  • Ding-Li Yu
    • 2
  1. 1.Department of AutomationNortheast University at QinhuangdaoChina
  2. 2.Control Systems Research Group, School of EngineeringLiverpool John Moores UniversityLiverpoolUK

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