Resumen

ROSSOMANDO, F.G.; DENTI F, J.  y  VIGLIOCCO, A.. Neural compensation and modelling of a hot strip rolling mill using radial basis function. Lat. Am. appl. res. [online]. 2011, vol.41, n.3, pp.241-248. ISSN 0327-0793.

In this paper a Neural Compensation Strategy for a hot rolling mill process is proposed. The target of this work is to built a RBF-NN compensation approximation for the classical force feed forward and speed controller. A strategy based on neural networks is proposed here, because they are capable of modelling many nonlinear systems and their neural control via RBF-NN approximation. Simulations demonstrate that the proposed solution deals with disturbances and modeling errors in a better way than classic solutions do. The analysis of the RBF-NN approximation error on the control errors is included, and control system performance is verified through simulations.

Palabras clave : Hot Strip Mill; Thickness Deviation; Neural Compensation; Radial Basis Functions.

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