Abstract
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.
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© 1995 Springer-Verlag London Limited
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Dubois, O., Nicolas, JL., Billat, A. (1995). A Radial Basis Function Network Model for the Adaptive Control of Drying Oven Temperature. In: Hunt, K.J., Irwin, G.R., Warwick, K. (eds) Neural Network Engineering in Dynamic Control Systems. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-3066-6_12
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DOI: https://doi.org/10.1007/978-1-4471-3066-6_12
Publisher Name: Springer, London
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