Neural compensation and modelling of a hot strip rolling mill using radial basis function

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 com- pensation 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.