A Radial Basis Function Network Model for the Adaptive Control of Drying Oven Temperature

  • Olivier Dubois
  • Jean-Louis Nicolas
  • Alain Billat
Part of the Advances in Industrial Control book series (AIC)


Artificial neural networks are new modelling tools for process control, especially in non-linear dynamic systems. They have been shown to successfully approximate non-linear relationships. This paper describes a neural control scheme for the temperature in a drying oven. The control strategy used is internal model control, in which the plant is modelled by a radial basis function network. The process was identified in an off-line phase —training the network while determining its optimal structure— and an on-line phase —adapting the neural model to any change in the process dynamics. The control strategy was tested experimentally in a series of trials with the oven empty and loaded.


Hide Layer Model Predictive Control Nonlinear Dynamic System Radial Basis Function Neural Network Radial Basis Function 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. Elanayar, S.V.T., Shin, Y.C.: Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems. IEEE Transactions on Neural Networks 5 (1994) 594–603.CrossRefGoogle Scholar
  2. Billings, S.A., Jamaluddin, H.B., Chen, S.: Properties of neural networks with applications to modelling non-linear dynamical systems. International Journal of Control 55 (1992) 193–224.MathSciNetzbMATHCrossRefGoogle Scholar
  3. Chen, S., Billings, S.A.: Neural networks for nonlinear dynamic system modelling and identification. International Journal of Control 56 (1992) 319–346.MathSciNetzbMATHCrossRefGoogle Scholar
  4. Chen, S., Billings, S.A., Grant, P.M.: Recursive hybrid algorithm for non-linear system identification using radial basis function networks. International Journal of control 55 (1992) 1051–1070.MathSciNetzbMATHCrossRefGoogle Scholar
  5. Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2 (1989) 183–192.CrossRefGoogle Scholar
  6. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2 (1989) 359–366.CrossRefGoogle Scholar
  7. Hunt, K.J., Sbarbaro, D.: Neural networks for nonlinear internal model control. IEE Proceedings-D 138 (1991) 431–438.zbMATHCrossRefGoogle Scholar
  8. Hunt, K.J., Sbarbaro, D., Zbikowski, R., Gawthrop, P.J.: Neural networks for control systems-A survey. Automatica 28 (1992) 1083–1112.MathSciNetzbMATHCrossRefGoogle Scholar
  9. Landau, I.D.: Identification et commande des systemes. (1993)zbMATHGoogle Scholar
  10. Hermes, Paris. Ljung, L.: System identification — Theory for the user. (1987) Prentice-Hail, Englewood Cliffs, London.Google Scholar
  11. Lowe, D., Webb, A.R.: Time series prediction by adaptive networks: a dynamical systems perspective. IEE Proceedings-F 138 (1991) 17–24.Google Scholar
  12. Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1 (1990) 4–27.CrossRefGoogle Scholar
  13. Park, J, Sanberg, I.W.: Universal approximation using radial basis function networks. Neural Computation 3 (1991) 246–257.CrossRefGoogle Scholar
  14. Powel, M.J.D.: Radial Basis functions for multivariable interpolation: a review. In J.C. Mason and M.G. Cox (Eds) Algorithms for Approximation (1987) 143–167 Clarendon Press, Oxford.Google Scholar
  15. Saint-Donat, J., Bhat, N., McAvoy, T.J.: Neural net based model predictive control. International Journal of Control 54 (1991) 1453–1468.zbMATHCrossRefGoogle Scholar
  16. Song, J.J., Park, S.: Neural model predictive control for nonlinear chemical processes. Journal of Chemical Engineering of Japan 26 (1993) 347–354.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 1995

Authors and Affiliations

  • Olivier Dubois
    • 1
  • Jean-Louis Nicolas
    • 1
  • Alain Billat
    • 1
  1. 1.Laboratoire d’Applications de la MicroélectroniqueUniversité de Reims Champagne ArdenneReims CedexFrance

Personalised recommendations